From 2e5d2f6efcb064e1af3889cc3c668bd45633ab90 Mon Sep 17 00:00:00 2001 From: vladvrx <93847468+vladvrx@users.noreply.github.com> Date: Fri, 11 Sep 2026 22:59:51 -0600 Subject: [PATCH 01/12] Upgrade BeatForge studio pipeline and RL workflow --- .../scripts/choreography.py | 135 ++++++- .../scripts/generate_map.py | 67 ++-- .../scripts/mapping_plan.py | 133 +++++++ .../beat-saber-mapping/scripts/rl/README.md | 148 ++++++++ .../beat-saber-mapping/scripts/rl/__init__.py | 15 +- .../scripts/rl/checkpoints.py | 58 +++ .../scripts/rl/environment.py | 117 +++--- .../beat-saber-mapping/scripts/rl/evaluate.py | 142 ++++++++ .../beat-saber-mapping/scripts/rl/features.py | 219 +++++++++++ .../beat-saber-mapping/scripts/rl/library.py | 46 +++ .../beat-saber-mapping/scripts/rl/models.py | 17 +- .../scripts/rl/policy_generator.py | 46 ++- .../beat-saber-mapping/scripts/rl/registry.py | 273 ++++++++++++++ .../beat-saber-mapping/scripts/rl/safety.py | 86 +++++ .../scripts/rl/train_custom_tracks.py | 224 +++++------- .../scripts/rl/train_ppo.py | 88 +++-- .../scripts/timing_export.py | 83 +++++ .../tests/test_rl_environment.py | 2 +- .../tests/test_rl_features.py | 115 ++++++ .../tests/test_rl_invariants.py | 113 ++++++ .../tests/test_rl_registry.py | 122 +++++++ .../tests/test_rl_training_correctness.py | 167 +++++++++ .github/workflows/skill-drift.yml | 2 +- .gitignore | 3 + README.md | 23 ++ Start-BeatForge.ps1 | 30 ++ docs/IMPROVEMENT_PLAN.md | 77 ++++ pyproject.toml | 2 + skill-lock.json | 31 +- .../scripts/choreography.py | 135 ++++++- .../scripts/generate_map.py | 67 ++-- .../scripts/mapping_plan.py | 133 +++++++ .../beat-saber-mapping/scripts/rl/README.md | 148 ++++++++ .../beat-saber-mapping/scripts/rl/__init__.py | 15 +- .../scripts/rl/checkpoints.py | 58 +++ .../scripts/rl/environment.py | 117 +++--- .../beat-saber-mapping/scripts/rl/evaluate.py | 142 ++++++++ .../beat-saber-mapping/scripts/rl/features.py | 219 +++++++++++ .../beat-saber-mapping/scripts/rl/library.py | 46 +++ .../beat-saber-mapping/scripts/rl/models.py | 17 +- .../scripts/rl/policy_generator.py | 46 ++- .../beat-saber-mapping/scripts/rl/registry.py | 273 ++++++++++++++ .../beat-saber-mapping/scripts/rl/safety.py | 86 +++++ .../scripts/rl/train_custom_tracks.py | 224 +++++------- .../scripts/rl/train_ppo.py | 88 +++-- .../scripts/timing_export.py | 83 +++++ .../tests/test_rl_environment.py | 2 +- .../tests/test_rl_features.py | 115 ++++++ .../tests/test_rl_invariants.py | 113 ++++++ .../tests/test_rl_registry.py | 122 +++++++ .../tests/test_rl_training_correctness.py | 167 +++++++++ src/beatforge/api.py | 250 +++++++++++-- src/beatforge/cli.py | 16 + src/beatforge/feedback_guidance.py | 120 ++++++ src/beatforge/learning.py | 261 ++++++++++++++ src/beatforge/mapping_plan.py | 13 + src/beatforge/premium.py | 80 +++- src/beatforge/preview.py | 341 ++++++++++++++++++ tests/test_api.py | 31 +- tests/test_browser.py | 51 ++- tests/test_demo_assets.py | 139 +++++++ tests/test_feedback_guidance.py | 52 +++ tests/test_job_lifecycle.py | 216 +++++++++++ tests/test_learning.py | 211 +++++++++++ tests/test_mapping_plan.py | 184 ++++++++++ tests/test_preview.py | 335 +++++++++++++++++ tests/test_studio_experience.py | 248 +++++++++++++ tests/test_webmcp.py | 15 +- tools/build_demo_assets.py | 204 +++++++++++ tools/build_static_demo.py | 12 +- tools/doctor.py | 59 +++ web/assets/demo/map.zip | Bin 0 -> 548460 bytes web/assets/demo/preview.json | 1 + web/assets/demo/song.ogg | Bin 0 -> 361088 bytes web/index.html | 107 ++++-- web/preview.js | 149 ++++++++ web/studio.css | 17 + web/studio.js | 74 ++++ web/webmcp.js | 6 +- 79 files changed, 7564 insertions(+), 628 deletions(-) create mode 100644 .codex/skills/beat-saber-mapping/scripts/mapping_plan.py create mode 100644 .codex/skills/beat-saber-mapping/scripts/rl/README.md create mode 100644 .codex/skills/beat-saber-mapping/scripts/rl/checkpoints.py create mode 100644 .codex/skills/beat-saber-mapping/scripts/rl/evaluate.py create mode 100644 .codex/skills/beat-saber-mapping/scripts/rl/features.py create mode 100644 .codex/skills/beat-saber-mapping/scripts/rl/library.py create mode 100644 .codex/skills/beat-saber-mapping/scripts/rl/registry.py create mode 100644 .codex/skills/beat-saber-mapping/scripts/rl/safety.py create mode 100644 .codex/skills/beat-saber-mapping/scripts/timing_export.py create mode 100644 .codex/skills/beat-saber-mapping/tests/test_rl_features.py create mode 100644 .codex/skills/beat-saber-mapping/tests/test_rl_invariants.py create mode 100644 .codex/skills/beat-saber-mapping/tests/test_rl_registry.py create mode 100644 .codex/skills/beat-saber-mapping/tests/test_rl_training_correctness.py create mode 100644 Start-BeatForge.ps1 create mode 100644 docs/IMPROVEMENT_PLAN.md create mode 100644 skills/beat-saber-mapping/scripts/mapping_plan.py create mode 100644 skills/beat-saber-mapping/scripts/rl/README.md create mode 100644 skills/beat-saber-mapping/scripts/rl/checkpoints.py create mode 100644 skills/beat-saber-mapping/scripts/rl/evaluate.py create mode 100644 skills/beat-saber-mapping/scripts/rl/features.py create mode 100644 skills/beat-saber-mapping/scripts/rl/library.py create mode 100644 skills/beat-saber-mapping/scripts/rl/registry.py create mode 100644 skills/beat-saber-mapping/scripts/rl/safety.py create mode 100644 skills/beat-saber-mapping/scripts/timing_export.py create mode 100644 skills/beat-saber-mapping/tests/test_rl_features.py create mode 100644 skills/beat-saber-mapping/tests/test_rl_invariants.py create mode 100644 skills/beat-saber-mapping/tests/test_rl_registry.py create mode 100644 skills/beat-saber-mapping/tests/test_rl_training_correctness.py create mode 100644 src/beatforge/feedback_guidance.py create mode 100644 src/beatforge/learning.py create mode 100644 src/beatforge/mapping_plan.py create mode 100644 src/beatforge/preview.py create mode 100644 tests/test_demo_assets.py create mode 100644 tests/test_feedback_guidance.py create mode 100644 tests/test_job_lifecycle.py create mode 100644 tests/test_learning.py create mode 100644 tests/test_mapping_plan.py create mode 100644 tests/test_preview.py create mode 100644 tests/test_studio_experience.py create mode 100644 tools/build_demo_assets.py create mode 100644 tools/doctor.py create mode 100644 web/assets/demo/map.zip create mode 100644 web/assets/demo/preview.json create mode 100644 web/assets/demo/song.ogg create mode 100644 web/preview.js create mode 100644 web/studio.css create mode 100644 web/studio.js diff --git a/.codex/skills/beat-saber-mapping/scripts/choreography.py b/.codex/skills/beat-saber-mapping/scripts/choreography.py index 9e14a45..0e8de44 100644 --- a/.codex/skills/beat-saber-mapping/scripts/choreography.py +++ b/.codex/skills/beat-saber-mapping/scripts/choreography.py @@ -25,6 +25,7 @@ ) from kinematics import bomb_on_swing_path, double_is_safe, swing_pose, transition_finding from official_corpus import map_features, normalize_beatmap +from mapping_plan import build_section_plan, normalize_mapping_plan from safety_contract import ( RECOVERY_BEATS, hold_span_beats, @@ -184,6 +185,8 @@ def select_intents(analysis: dict[str, Any], sections: dict[str, Any], difficult ) gap = config.peak_min_gap if is_peak else config.min_gap adaptive_gap = max(gap, gap * (1.15 - 0.35 * intensity)) + density = float(section.get("density", 1.0)) + adaptive_gap = max(gap, adaptive_gap / max(0.35, density)) if beat - last_beat + 1e-9 < adaptive_gap: continue if any( @@ -194,13 +197,16 @@ def select_intents(analysis: dict[str, Any], sections: dict[str, Any], difficult continue strength = float(event.get("strength", 0.0)) energies = event.get("stemEnergy") if isinstance(event.get("stemEnergy"), dict) else {} + dominant = str(section.get("dominantInstrument", "auto")) + if dominant != "auto" and dominant in energies: + strength *= 0.7 + 1.1 * max(0.0, min(1.0, float(energies[dominant]))) layer = str(event.get("layer") or "") instrument = float(energies.get("drums") or 0.0) + float(energies.get("bass") or 0.0) + float(energies.get("guitar") or 0.0) vocal = float(energies.get("vocals") or 0.0) layered = bool(energies) and instrument >= vocal * 1.15 and layer in {"drums", "bass", "guitar", "piano", "other"} if difficulty in {"Expert", "ExpertPlus"} and layered: strength *= 1.35 - threshold = base_threshold + threshold = base_threshold / max(0.35, density) if is_peak: threshold *= 0.88 if difficulty in {"Expert", "ExpertPlus"} else 0.94 elif intensity < 0.4: @@ -256,6 +262,15 @@ def select_intents(analysis: dict[str, Any], sections: dict[str, Any], difficult "layer": layer or "mix", "stemEnergy": energies, } + if "motifId" in section: + relative = beat - float(section.get("startBeat", 0.0)) + motif_offset = int(section.get("motifOffset", 0)) + payload.update({ + "sectionId": section["id"], "motifId": section["motifId"], + "patternBeat": relative + motif_offset * 4.0, + "patternIndex": int(round(relative * 2.0)) + motif_offset, + "style": section.get("style", "balanced"), + }) selected.append(payload) last_beat = beat return selected @@ -433,6 +448,7 @@ def _candidate_notes( index: int, intensity: float, beat: float, + preference_beat: float | None = None, ) -> list[dict[str, Any]]: """Enumerate combo-first arrow poses. Dots are a rare reset, not the default cut.""" @@ -440,8 +456,9 @@ def _candidate_notes( home = (0, 1) else: home = (3, 2) - preferred = swing_family(color, parity, beat) - lead = cut_for(color, parity, index, beat) + phase = beat if preference_beat is None else preference_beat + preferred = swing_family(color, parity, phase) + lead = cut_for(color, parity, index, phase) notes: list[dict[str, Any]] = [] seen: set[tuple[int, int, int]] = set() @@ -627,9 +644,14 @@ def _pose_domain( intensity = float(intent.get("intensity", 0.5)) kind = intent["kind"] duration = float(intent.get("duration") or 0.0) - heads = _candidate_notes(color, parity, index, intensity, beat) + index = int(intent.get("patternIndex", index)) + int(intent.get("candidateVariant", 0)) + phase = float(intent.get("patternBeat", beat)) + heads = _candidate_notes(color, parity, index, intensity, beat, preference_beat=phase) + if intent.get("style") in {"flow", "chill"}: + home_x = 0.5 if color == 0 else 2.5 + heads.sort(key=lambda head: abs(float(head["x"]) - home_x) + (0.5 if int(head["d"]) in {2, 3, 8} else 0.0)) if str(intent.get("layer") or "") == "combo-stack": - lead = cut_for(color, parity, index, beat) + lead = cut_for(color, parity, index, phase) if color == 0: pair_cells = ((1, 1), (0, 1), (1, 0), (1, 2)) else: @@ -667,7 +689,9 @@ def _joint_combined_domain( beat = float(intent["beat"]) stack = str(intent.get("layer") or "") == "combo-stack" - lead_parity = int(beat // 4.0) % 2 if stack else (index + int(beat // 2.0)) % 2 + pattern_beat = float(intent.get("patternBeat", beat)) + pattern_index = int(intent.get("patternIndex", index)) + int(intent.get("candidateVariant", 0)) + lead_parity = int(pattern_beat // 4.0) % 2 if stack else (pattern_index + int(pattern_beat // 2.0)) % 2 buckets = { (color_id, parity): _pose_domain(intent, color_id, parity, index, bpm, recovery_beats)[:6] for color_id in (0, 1) @@ -2500,12 +2524,13 @@ def score_candidate(database_path: Path | None, features: dict[str, Any]) -> dic } -def generate_all( +def _generate_single_pass( analysis: dict[str, Any], sections: dict[str, Any], seed: int, corpus_database: Path | None = None, difficulties: Sequence[str] | None = None, + candidate_variant: int = 0, ) -> tuple[dict[str, dict[str, Any]], dict[str, Any]]: generated: dict[str, dict[str, Any]] = {} report: dict[str, Any] = {"solver": {}, "difficulties": {}, "officialReferences": {}} @@ -2513,6 +2538,8 @@ def generate_all( for difficulty in chosen: emit_progress("choreography", f"selecting {difficulty} intents from onsets and sections") intents = select_intents(analysis, sections, difficulty) + if candidate_variant: + intents = [{**intent, "candidateVariant": candidate_variant} for intent in intents] joint = None try: emit_progress("choreography", f"solving joint CP-SAT for {difficulty} ({len(intents)} events)") @@ -2559,3 +2586,97 @@ def generate_all( ) report["difficulties"][difficulty]["corpusRank"] = score_candidate(corpus_database, candidate_features) return generated, report + + +def rank_choreography_candidate( + beatmap: dict[str, Any], difficulty: str, bpm: float, + corpus_rank: dict[str, Any], reference: dict[str, Any], + expected_count: int, +) -> dict[str, Any]: + """Score complete candidates only after running the unchanged structural gates.""" + from beatforge_core import ValidationReport + from validate_map import validate_v3 + + validation = ValidationReport() + validate_v3(beatmap, f"{difficulty}Standard.dat", validation, bpm=bpm, difficulty=difficulty) + notes = beatmap.get("colorNotes", []) + errors = [issue.code for issue in validation.errors] + if expected_count and not notes: + errors.append("NO_PLAYABLE_NOTES") + events, _ = normalize_beatmap(beatmap) + features = map_features(events, bpm) + profile = reference.get("profile", {}) + comparable = [name for name in ("p95Reach", "centerVisionRate", "medianSameHandGap") if name in profile] + profile_distance = sum(abs(float(features.get(name, 0.0)) - float(profile[name])) / max(0.25, abs(float(profile[name]))) for name in comparable) / max(1, len(comparable)) + reference_score = 1.0 / (1.0 + profile_distance) if comparable else 0.5 + corpus_score = float(corpus_rank.get("score", 50.0)) / 100.0 + coverage = min(1.0, len(notes) / max(1, expected_count)) + signatures = [(int(note["x"]), int(note["y"]), int(note["d"])) for note in notes] + repeats = sum(left == right for left, right in zip(signatures, signatures[1:])) + variety = 1.0 - repeats / max(1, len(signatures) - 1) + score = 100.0 * (0.4 * corpus_score + 0.25 * reference_score + 0.25 * coverage + 0.1 * variety) + return { + "score": round(score, 6), "hardErrors": sorted(set(errors)), + "eligible": not errors, "corpusScore": round(corpus_score * 100.0, 6), + "referenceScore": round(reference_score * 100.0, 6), + "eventCoverage": round(coverage, 6), "variety": round(variety, 6), + "features": features, + } + + +def select_ranked_candidate(candidates: list[dict[str, Any]]) -> dict[str, Any]: + eligible = [candidate for candidate in candidates if candidate.get("eligible")] + if not eligible: + codes = sorted({code for candidate in candidates for code in candidate.get("hardErrors", [])}) + raise ValueError("No candidate passed the unchanged safety gates: " + ", ".join(codes)) + return max(eligible, key=lambda candidate: (candidate["score"], -candidate["index"])) + + +def generate_all( + analysis: dict[str, Any], sections: dict[str, Any], seed: int, + corpus_database: Path | None = None, + difficulties: Sequence[str] | None = None, + mapping_plan: dict[str, Any] | None = None, +) -> tuple[dict[str, dict[str, Any]], dict[str, Any]]: + if mapping_plan is None: + return _generate_single_pass(analysis, sections, seed, corpus_database, difficulties) + controls = normalize_mapping_plan(mapping_plan) + planned = build_section_plan(analysis, sections, controls) + chosen = tuple(name for name in DIFFICULTIES if name in set(difficulties or DIFFICULTIES)) or DIFFICULTIES + generated: dict[str, dict[str, Any]] = {} + report: dict[str, Any] = {"solver": {}, "difficulties": {}, "officialReferences": {}, "candidateRanking": {}, "mappingPlan": planned} + for difficulty in chosen: + # Retrieve before generation so every candidate is compared with the same reference family. + reference = retrieve_official_references(corpus_database, difficulty, float(analysis["bpm"])) + intents = select_intents(analysis, planned, difficulty) + candidates = [] + payloads: dict[int, tuple[dict[str, Any], dict[str, Any]]] = {} + for index in range(controls["candidateCount"]): + emit_progress("choreography", f"{difficulty}: generating candidate {index + 1}/{controls['candidateCount']}") + try: + maps, trace = _generate_single_pass(analysis, planned, seed + index * 7919, corpus_database, (difficulty,), candidate_variant=index) + beatmap = maps[difficulty] + if controls["noBombs"]: + beatmap["bombNotes"] = [] + if controls["noWalls"]: + beatmap["obstacles"] = [] + ranked_map = beatmap + if len(analysis.get("beatGrid", [])) >= 2: + from timing_export import project_map_timing + ranked_map = project_map_timing(beatmap, analysis) + ranked_events, _ = normalize_beatmap(ranked_map) + corpus_rank = score_candidate(corpus_database, map_features(ranked_events, float(analysis["bpm"]))) + rank = rank_choreography_candidate(ranked_map, difficulty, float(analysis["bpm"]), corpus_rank, reference, len(intents)) + trace["difficulties"][difficulty]["corpusRank"] = corpus_rank + candidates.append({"index": index, **rank}) + payloads[index] = (beatmap, trace) + except (RuntimeError, ValueError) as error: + candidates.append({"index": index, "eligible": False, "score": 0.0, "hardErrors": [str(error)]}) + winner = select_ranked_candidate(candidates) + beatmap, trace = payloads[winner["index"]] + generated[difficulty] = beatmap + report["solver"][difficulty] = trace["solver"][difficulty] + report["difficulties"][difficulty] = trace["difficulties"][difficulty] + report["officialReferences"][difficulty] = reference + report["candidateRanking"][difficulty] = {"selectedIndex": winner["index"], "candidates": candidates} + return generated, report diff --git a/.codex/skills/beat-saber-mapping/scripts/generate_map.py b/.codex/skills/beat-saber-mapping/scripts/generate_map.py index 81d79be..f189144 100644 --- a/.codex/skills/beat-saber-mapping/scripts/generate_map.py +++ b/.codex/skills/beat-saber-mapping/scripts/generate_map.py @@ -17,6 +17,8 @@ from beatforge_core import DIFFICULTIES, TOOL_VERSION, default_cache_dir, difficulty_rank, emit_progress, encode_ogg, find_ffmpeg, load_json, parse_difficulties, sha256_file, write_json from choreography import CONFIGS, generate_all +from mapping_plan import build_section_plan, normalize_mapping_plan +from timing_export import project_map_timing, project_plan_timing from artwork import derive_palette, extract_embedded_artwork, lookup_itunes_cover, lookup_release_cover, palette_from_rgb, write_palette_cover from official_corpus import corpus_is_fresh, detect_game_root, sync as sync_official_corpus @@ -373,6 +375,7 @@ def main() -> int: parser.add_argument("--profile", default="official-premium", choices=["official-premium", "official-rl"]) parser.add_argument("--engine", default="cp-sat", choices=["cp-sat", "rl", "hybrid"], help="Choreography engine to use (cp-sat, rl, hybrid)") parser.add_argument("--rl-model", type=Path, help="Path to trained PyTorch RL policy checkpoint") + parser.add_argument("--mapping-plan", type=Path, help="Version 1 structured creative controls JSON") parser.add_argument("--environment", default=None, help="Beat Saber environment name (e.g. DaftPunkEnvironment, DefaultEnvironment)") parser.add_argument("--full-spread", action="store_true", default=True) parser.add_argument( @@ -393,6 +396,13 @@ def main() -> int: parser.add_argument("--game-root", type=Path, help="Beat Saber install root; auto-detected on common Oculus/Steam paths") parser.add_argument("--seed", type=int) args = parser.parse_args() + try: + creative_controls = normalize_mapping_plan(load_json(args.mapping_plan)) if args.mapping_plan else None + if (args.engine == "rl" or args.profile == "official-rl") and (args.rl_model is None or not args.rl_model.is_file()): + raise ValueError("The RL engine requires an existing trained checkpoint supplied with --rl-model") + except (ValueError, OSError) as error: + print(f"ERROR: {error}", file=sys.stderr) + return 2 if not args.audio.is_file(): print(f"ERROR: audio file does not exist: {args.audio}", file=sys.stderr) return 2 @@ -423,6 +433,7 @@ def main() -> int: emit_progress("timing", "reusing existing analysis.json") analysis = load_json(analysis_file) sections = load_json(sections_file) + creative_plan = build_section_plan(analysis, sections, creative_controls) if creative_controls is not None else None unconfirmed = bool(args.continue_unconfirmed) if analysis.get("status") != "timing_verified": if not unconfirmed: @@ -496,54 +507,33 @@ def main() -> int: emit_progress("choreography", f"solving {', '.join(chosen)}") if args.engine == "rl" or args.profile == "official-rl": from rl.policy_generator import RLMapGenerator + from rl.features import load_analysis_features rl_gen = RLMapGenerator(model_path=args.rl_model) - raw_bg = load_json(analysis_dir / "beat_grid.json") if (analysis_dir / "beat_grid.json").exists() else None - if isinstance(raw_bg, dict) and "tempoRegions" in raw_bg: - max_beat = max((float(r.get("endBeat", 0.0)) for r in raw_bg.get("tempoRegions", [])), default=0.0) - beat_grid = [round(i * 0.25, 4) for i in range(max(16, int(max_beat * 4)))] - elif isinstance(raw_bg, list): - beat_grid = [float(b) for b in raw_bg] - else: - duration_s = float(analysis.get("durationSeconds", analysis.get("duration", 240.0))) - bpm_val = float(analysis.get("bpm", 125.0)) - beat_grid = [round(i * 0.25, 4) for i in range(max(16, int(duration_s * bpm_val / 60.0 * 4)))] - - grid_len = len(beat_grid) - try: - import numpy as np - from beatforge_core import frame_features, load_audio - audio_buf = load_audio(args.audio) - feat = frame_features(audio_buf.samples) - flux = feat["flux"] - onsets = np.interp(np.linspace(0, len(flux), grid_len), np.arange(len(flux)), flux) - onsets = ((onsets - np.min(onsets)) / (np.max(onsets) - np.min(onsets) + 1e-6)).tolist() - except Exception: - onsets = [0.5] * grid_len - - audio_features = { - "onsets": onsets, - "flux": onsets, - "stems": { - "drums": onsets, - "bass": onsets, - "vocals": [o * 0.7 for o in onsets], - "guitar": [o * 0.5 for o in onsets], - "piano": [o * 0.5 for o in onsets], - "other": [o * 0.4 for o in onsets], - }, - "sections": [s.get("type", "verse") for s in sections] if isinstance(sections, list) else ["verse"] * grid_len, - } + bundle = load_analysis_features(analysis_dir, require_verified=not unconfirmed) maps = {} for diff in chosen: - maps[diff] = rl_gen.generate_difficulty(audio_features, beat_grid, float(analysis["bpm"]), difficulty=diff) + maps[diff] = rl_gen.generate_difficulty(bundle.audio_features, bundle.beat_grid, bundle.bpm, difficulty=diff, seed=seed) + if creative_controls and creative_controls["noBombs"]: + maps[diff]["bombNotes"] = [] + if creative_controls and creative_controls["noWalls"]: + maps[diff]["obstacles"] = [] choreography_report = { "poseSolver": "rl-policy-v1", "solver": "rl-policy-v1", "difficulties": chosen, + "checkpoint": rl_gen.checkpoint_metadata, + "features": bundle.provenance, + "mappingPlan": creative_plan, + "creativeControlSupport": "RL applies noBombs and noWalls only; phrase, density, and style controls require the premium engine", } else: - maps, choreography_report = generate_all(analysis, sections, seed, args.corpus_database, difficulties=chosen) + maps, choreography_report = generate_all(analysis, sections, seed, args.corpus_database, difficulties=chosen, mapping_plan=creative_controls) + maps = {difficulty: project_map_timing(payload, analysis) for difficulty, payload in maps.items()} + if creative_plan is not None: + creative_plan = project_plan_timing(creative_plan, analysis) + choreography_report["mappingPlan"] = creative_plan + write_json(package_analysis_dir / "mapping_plan.json", creative_plan) for difficulty, payload in maps.items(): write_json(args.out / f"{difficulty}Standard.dat", payload) emit_progress("audio", "encoding song.ogg for the Beat Saber pack") @@ -590,6 +580,7 @@ def main() -> int: }, }, "choreography": choreography_report, + "mappingPlan": creative_plan, "officialCorpus": {"database": str(args.corpus_database.resolve()), **corpus_report}, "documentation": { "manifest": "_beatforge/documentation_manifest.json", diff --git a/.codex/skills/beat-saber-mapping/scripts/mapping_plan.py b/.codex/skills/beat-saber-mapping/scripts/mapping_plan.py new file mode 100644 index 0000000..7e4a0bb --- /dev/null +++ b/.codex/skills/beat-saber-mapping/scripts/mapping_plan.py @@ -0,0 +1,133 @@ +"""Versioned, deterministic creative controls shared by Studio and the portable mapper.""" + +from __future__ import annotations + +import math +import re +from typing import Any + +SCHEMA_VERSION = 1 +INSTRUMENTS = ("auto", "drums", "bass", "vocals", "guitar", "piano", "other") +STYLES = ("balanced", "flow", "tech", "chill") +SCALE_FIELDS = ("intensity", "density", "verseDensity", "chorusDensity") +DEFAULTS = { + "schemaVersion": SCHEMA_VERSION, "brief": "", "intensity": 1.0, + "density": 1.0, "verseDensity": 1.0, "chorusDensity": 1.0, + "dominantInstrument": "auto", "style": "balanced", "noBombs": False, + "noWalls": False, "candidateCount": 2, "sectionOverrides": [], +} + + +def _scale(value: Any, field: str) -> float: + if isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value) or not 0.5 <= value <= 1.5: + raise ValueError(f"{field} must be a number from 0.5 to 1.5") + return float(value) + + +def normalize_mapping_plan(value: dict[str, Any] | None = None) -> dict[str, Any]: + """Resolve supported brief phrases, with explicit controls taking precedence.""" + if value is None: + value = {} + if not isinstance(value, dict): + raise ValueError("mapping plan must be an object") + unknown = set(value) - set(DEFAULTS) - {"interpretation"} + if unknown: + raise ValueError("unknown mapping plan controls: " + ", ".join(sorted(unknown))) + if type(value.get("schemaVersion", 1)) is not int or value.get("schemaVersion", 1) != 1: + raise ValueError("mapping plan schemaVersion must be 1") + brief = value.get("brief", "") + if not isinstance(brief, str) or len(brief) > 2000: + raise ValueError("brief must be text of at most 2000 characters") + text = brief.casefold() + inferred: dict[str, Any] = {} + recognized: list[str] = [] + phrases = [ + (r"\b(?:no|without) bombs\b", "noBombs", True, "No bombs"), + (r"\b(?:no|without) walls\b", "noWalls", True, "No walls"), + (r"\b(?:sparse|lighter|easier) verses?\b", "verseDensity", 0.75, "Lighter verses"), + (r"\b(?:dense|denser|bigger|stronger) chorus(?:es)?\b", "chorusDensity", 1.25, "Denser choruses"), + (r"\b(?:less|fewer) notes\b", "density", 0.75, "Fewer notes"), + (r"\bmore notes\b", "density", 1.25, "More notes"), + (r"\b(?:relaxed|chill)\b", "style", "chill", "Chill style"), + (r"\b(?:flow|flowing|smooth)\b", "style", "flow", "Flow style"), + (r"\b(?:tech|technical)\b", "style", "tech", "Tech style"), + ] + for pattern, field, setting, description in phrases: + if re.search(pattern, text): + inferred[field] = setting + if field not in value: + recognized.append(description) + for instrument in INSTRUMENTS[1:]: + if re.search(r"\b(?:follow|focus on|emphasize) (?:the )?" + instrument + r"\b", text): + inferred["dominantInstrument"] = instrument + if "dominantInstrument" not in value: + recognized.append("Follow " + instrument) + result = {**DEFAULTS, **inferred, **value, "brief": brief} + for field in SCALE_FIELDS: + result[field] = _scale(result[field], field) + if result["dominantInstrument"] not in INSTRUMENTS: + raise ValueError("unknown dominantInstrument") + if result["style"] not in STYLES: + raise ValueError("unknown mapping style") + for field in ("noBombs", "noWalls"): + if not isinstance(result[field], bool): + raise ValueError(f"{field} must be a boolean") + count = result["candidateCount"] + if isinstance(count, bool) or not isinstance(count, int) or not 1 <= count <= 3: + raise ValueError("candidateCount must be an integer from 1 to 3") + overrides = result["sectionOverrides"] + if not isinstance(overrides, list) or len(overrides) > 200: + raise ValueError("sectionOverrides must be a list with at most 200 entries") + checked = [] + seen = set() + for override in overrides: + if not isinstance(override, dict) or set(override) - {"id", "density", "intensity", "dominantInstrument"}: + raise ValueError("invalid section override") + section_id = override.get("id") + if not isinstance(section_id, str) or not section_id or len(section_id) > 100 or section_id in seen: + raise ValueError("section overrides need unique nonempty ids") + seen.add(section_id) + item = dict(override) + for field in ("density", "intensity"): + if field in item: + item[field] = _scale(item[field], "section " + field) + if "dominantInstrument" in item and item["dominantInstrument"] not in INSTRUMENTS: + raise ValueError("unknown section dominantInstrument") + checked.append(item) + result["sectionOverrides"] = checked + interpretation = value.get("interpretation", []) + if not isinstance(interpretation, list) or not all(isinstance(item, str) for item in interpretation): + raise ValueError("interpretation must be a list of phrases") + result["interpretation"] = recognized or list(interpretation) + return result + + +def build_section_plan(analysis: dict[str, Any], sections: dict[str, Any], value: dict[str, Any]) -> dict[str, Any]: + plan = normalize_mapping_plan(value) + source = sections.get("sections") or [{"label": "body", "startBeat": 0.0, "endBeat": float(analysis.get("durationSeconds", 0.0)) * float(analysis.get("bpm", 120.0)) / 60.0, "intensity": 0.5}] + overrides = {entry["id"]: entry for entry in plan["sectionOverrides"]} + style_scale = {"balanced": 1.0, "flow": 0.95, "tech": 1.05, "chill": 0.75}[plan["style"]] + planned = [] + for index, section in enumerate(source): + item = dict(section) + section_id = str(item.get("id") or f"section-{index:03d}") + label = str(item.get("label") or item.get("type") or "body") + family = re.sub(r"[\d\W_]+", "", label.casefold()) or "body" + chorus = any(word in family for word in ("chorus", "drop", "peak")) + verse = "verse" in family + factor = plan["chorusDensity"] if chorus else plan["verseDensity"] if verse else 1.0 + override = overrides.get(section_id, {}) + density = plan["density"] * factor * style_scale * override.get("density", 1.0) + item.update({ + "id": section_id, "label": label, "motifId": family, + "motifOffset": sum((i + 1) * ord(character) for i, character in enumerate(family)) % 4, + "density": round(max(0.35, min(1.75, density)), 6), + "intensity": max(0.0, min(1.0, float(item.get("intensity", 0.5)) * plan["intensity"] * override.get("intensity", 1.0))), + "dominantInstrument": override.get("dominantInstrument", plan["dominantInstrument"]), + "style": plan["style"], + }) + planned.append(item) + missing = set(overrides) - {item["id"] for item in planned} + if missing: + raise ValueError("unknown section ids: " + ", ".join(sorted(missing))) + return {"schemaVersion": 1, "source": sections.get("source", "internal"), "sections": planned, "controls": plan} diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/README.md b/.codex/skills/beat-saber-mapping/scripts/rl/README.md new file mode 100644 index 0000000..2b121cf --- /dev/null +++ b/.codex/skills/beat-saber-mapping/scripts/rl/README.md @@ -0,0 +1,148 @@ +# RL training, evaluation, and checkpoint promotion + +RL remains experimental. The studio's constraint solver is the default. A saved +PPO reward is not proof of musical quality, and generated maps still require the +normal package validator and human headset checks. + +Set `PYTHONPATH` to `skills/beat-saber-mapping/scripts` and use the project's Python +environment. PyTorch is an optional dependency. These commands operate on local +files and do not download models, analyze new audio, or install songs. + +## Prepare a song library + +Run `analyze_audio.py` and resolve the normal timing gate first. Every song needs +`analysis.json`, `beat_grid.json`, `sections.json`, and `audio_features.npz`. +Training and evaluation require `timing_verified`. The studio's explicit +unconfirmed-generation override is the only caller that disables this check, +and its feature provenance continues to say `needs_anchors`. + +Use a manifest outside the repository for your private audio library: + +```json +{ + "schemaVersion": 1, + "tracks": [ + {"id": "training-song", "split": "train", "analysisDir": "analysis/training-song"}, + {"id": "validation-song", "split": "validation", "analysisDir": "analysis/validation-song"}, + {"id": "final-test-song", "split": "test", "analysisDir": "analysis/final-test-song"} + ] +} +``` + +Paths resolve relative to the manifest. IDs and analyzed audio SHA-256 hashes +must be unique across the entire library. Use varied songs and keep the final +test split untouched while selecting models. Audio, analysis, maps, checkpoints, +and evaluation outputs stay out of version control. + +## Train with the same inputs used at generation + +```text +python -m rl.train_custom_tracks --manifest library.json --rounds 1 --timesteps-per-track 16384 --seed 42 --out data/models/candidate.pt +python -m rl.train_ppo --analysis-dir analysis/song --timesteps 16384 --seed 42 --out data/models/single-song.pt +``` + +`--analysis-dir` can be repeated for library training without a manifest. +`--no-resume` explicitly starts fresh. Resuming a real-song run from a checkpoint +without recorded training hashes is rejected because held-out independence +cannot be established. Old `--ram-dir`, `--ninajirachi-dir`, and `--spotify-dir` +discovery was replaced by explicit artifact directories or a manifest. + +The training loop uses the requested number of transitions exactly. Full song +duration stays in the environment; a short requested training budget may still +cover only part of a song. The default is one library round. It no longer silently +performs 99 rounds. Seeds cover initialization, policy sampling, and batch order. +Repeatability applies to the same device and runtime versions. + +The second hand's action mask uses the first hand's selected note. Simultaneous +handclaps, intersecting saber paths, and center vision doubles are masked during +PPO collection and inference. PPO stores that conditional mask for replay, so +the action probability used during optimization matches collection. This mask +does not replace validation of holds, walls, bombs, and the final package. + +For a software smoke test, use the explicit `--synthetic` option on `train_ppo`. +That pulse has no separated stems or verified song timing and cannot establish +held-out song performance. + +`rl.features.load_analysis_features(path)` returns `AnalysisFeatures` with +`audio_features`, `beat_grid`, `bpm`, `provenance`, and the original `analysis`. +The schema is `beatforge-rl-sample-grid-v2`. Quarter-beat decision samples come +from the monotonic integer sample grid, including offsets and tempo changes. +Frame interpolation uses saved sample positions, seconds, or `hopSamples`. +`sections.json.sections` supplies interval labels. Sample and second boundaries +take precedence over derived beat fields. Real Demucs event `stemEnergy` +annotations enter their nearest decision step. Missing stems remain zero and +`stemAvailability` records which stems actually exist. Missing spectral flux is +zero with `fluxAvailable: false`; the mix onset envelope is never relabeled as a +separated instrument. The 69-value policy observation layout remains compatible; +availability flags and provenance are metadata, while section labels influence +the musical reward. + +Checkpoints contain `state_dict` and JSON-compatible `metadata`, including the +feature schema, seed, training audio hashes, and analysis provenance. Saves use +an atomic replacement. Inference uses `weights_only=True`, checks tensor shapes +and finite values, and records the exact checkpoint SHA-256. Legacy raw state +dicts can still be loaded explicitly for generation with +`featureSchema: legacy-unrecorded`. They cannot enter a verified held-out +comparison. A missing or incompatible checkpoint never falls back to a random +policy. Callers supplying a policy instance directly are marked +`source: supplied-policy`, for tests or explicit application integration. + +## Compare on held-out songs + +```text +python -m rl.evaluate --manifest library.json --checkpoint data/models/baseline.pt --checkpoint data/models/candidate.pt --split validation --difficulty Expert --seed 42 --out data/evaluations/comparison.json +``` + +The command checks checkpoint training hashes against held-out audio hashes, +generates each chart deterministically, projects it onto the exact audio clock, +and runs the shared chart validator. It reports hard errors, warning codes, +NPS, hand imbalance, repeated transitions, section density, and the mean onset +strength at note positions. Empty charts count as failures. The report includes +sample artifact hashes, checkpoint hashes, evaluator code hashes, runtime +versions, chart hashes, and the seed. Exit status 2 means a generated chart has +hard errors; the JSON report is still saved. + +These measurements do not replace packaged-map checks or human evaluation. +Blind preferences, manual corrections, and headset playtests are separate +evidence. No generated report claims those checks happened. + +## Promote and roll back + +```text +python -m rl.registry --registry data/model_registry initialize --checkpoint data/models/baseline.pt --evaluation data/evaluations/comparison.json +python -m rl.registry --registry data/model_registry promote --checkpoint data/models/candidate.pt --evaluation data/evaluations/comparison.json +python -m rl.registry --registry data/model_registry active +python -m rl.registry --registry data/model_registry rollback +``` + +Initialization explicitly selects a validated, nonempty baseline. Promotion +requires identical manifest, split, song analysis hashes, difficulty, seed, +evaluator code, feature schema, and runtime versions. Each candidate chart must +have zero hard errors and no increase in warnings, hand imbalance, repeated +transition fraction, or distance from the difficulty's target NPS. No held-out +song may regress in onset alignment, and the mean onset-strength gain must be +at least 0.001. This is a measured candidate improvement, not proof that humans +prefer it. If the benchmark or evaluator changes, evaluate both models again +and explicitly establish a new registry baseline. + +The registry copies checkpoints and evaluations into folders named by their +content hashes. `registry.json` contains `schemaVersion`, `active`, `models`, +and an append-only `history` of selections. Promotion atomically switches the +active reference and retains prior files for rollback. It does not delete or +overwrite the source checkpoints. Concurrent writes fail with a lock error. +`active_checkpoint(registry_root)` verifies the active file's hash before +returning its path. A feedback-triggered local job can call +`register_checkpoint(root, checkpoint, evaluation)` after an explicitly run +training/evaluation cycle; this module starts no background work. + +## Focused checks + +```text +python -m pytest skills/beat-saber-mapping/tests/test_rl_features.py skills/beat-saber-mapping/tests/test_rl_training_correctness.py skills/beat-saber-mapping/tests/test_rl_registry.py skills/beat-saber-mapping/tests/test_rl_environment.py +``` + +Tests cover offset and tempo-change sampling, localized real stems, missing +inputs, section intervals, train/eval content leakage, checkpoint corruption, +terminal PPO bootstrap boundaries, exact budgets, seeds, promotion rejection, +and artifact-preserving rollback. They use small synthetic fixtures rather than +training on or redistributing private song audio. diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/__init__.py b/.codex/skills/beat-saber-mapping/scripts/rl/__init__.py index b1b74ac..5a22b08 100644 --- a/.codex/skills/beat-saber-mapping/scripts/rl/__init__.py +++ b/.codex/skills/beat-saber-mapping/scripts/rl/__init__.py @@ -2,9 +2,18 @@ from __future__ import annotations -from .environment import BeatSaberEnv -from .models import ActorCriticPolicy, BeatForgeAudioEncoder -from .rewards import CompositeReward, KinematicReward, MusicalReward, StyleReward +def __getattr__(name): + # Feature extraction and benchmark metric helpers work without PyTorch. + if name in {"ActorCriticPolicy", "BeatForgeAudioEncoder"}: + from . import models + return getattr(models, name) + if name == "BeatSaberEnv": + from .environment import BeatSaberEnv + return BeatSaberEnv + if name in {"CompositeReward", "KinematicReward", "MusicalReward", "StyleReward"}: + from . import rewards + return getattr(rewards, name) + raise AttributeError(name) __all__ = [ "BeatSaberEnv", diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/checkpoints.py b/.codex/skills/beat-saber-mapping/scripts/rl/checkpoints.py new file mode 100644 index 0000000..bb3fe62 --- /dev/null +++ b/.codex/skills/beat-saber-mapping/scripts/rl/checkpoints.py @@ -0,0 +1,58 @@ +"""Validated policy weights and portable, non-pickle provenance metadata.""" + +from __future__ import annotations + +from pathlib import Path +import json +from typing import Any + +import torch + +from .features import FEATURE_SCHEMA, file_sha256 +from .models import ActorCriticPolicy + + +def load_checkpoint(path: Path, device: str = "cpu") -> tuple[ActorCriticPolicy, dict[str, Any]]: + path = Path(path) + if not path.is_file(): + raise FileNotFoundError(f"RL checkpoint does not exist: {path}") + try: + payload = torch.load(path, map_location=device, weights_only=True) + if not isinstance(payload, dict): + raise ValueError("checkpoint must contain policy weights") + wrapped = "state_dict" in payload + weights = payload["state_dict"] if wrapped else payload + metadata = dict(payload.get("metadata", {})) if wrapped else {"featureSchema": "legacy-unrecorded"} + json.dumps(metadata, allow_nan=False) + for name in ("trainingSourceSha256", "heldOutSourceSha256"): + if name in metadata: + hashes = metadata[name] + if (not isinstance(hashes, list) or not all(isinstance(value, str) and len(value) == 64 + and all(character in "0123456789abcdef" for character in value) for value in hashes)): + raise ValueError(f"Invalid checkpoint lineage field: {name}") + schema = metadata.get("featureSchema", "legacy-unrecorded") + if schema not in {FEATURE_SCHEMA, "legacy-unrecorded"}: + raise ValueError(f"unsupported feature schema: {schema}") + first = weights["encoder.0.weight"] + if first.ndim != 2 or first.shape[1] != 69: + raise ValueError("checkpoint observation dimensions are incompatible with the RL environment") + with torch.random.fork_rng(devices=[]): + policy = ActorCriticPolicy(obs_dim=int(first.shape[1]), hidden_dim=int(first.shape[0])) + if not all(isinstance(value, torch.Tensor) and torch.is_floating_point(value) and torch.isfinite(value).all() for value in weights.values()): + raise ValueError("checkpoint contains non-finite or invalid weights") + policy.load_state_dict(weights, strict=True) + except Exception as exc: + raise ValueError(f"Invalid RL checkpoint {path.name}: {exc}") from exc + policy.to(device) + policy.eval() + metadata.update({"checkpointFile": path.name, "checkpointSha256": file_sha256(path)}) + return policy, metadata + + +def save_checkpoint(policy: ActorCriticPolicy, path: Path, metadata: dict[str, Any]) -> None: + """Replace checkpoints atomically so interruption cannot destroy a prior save.""" + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(path.name + ".tmp") + torch.save({"state_dict": policy.state_dict(), "metadata": {**metadata, "featureSchema": FEATURE_SCHEMA}}, temporary) + temporary.replace(path) diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/environment.py b/.codex/skills/beat-saber-mapping/scripts/rl/environment.py index db1d13f..4870237 100644 --- a/.codex/skills/beat-saber-mapping/scripts/rl/environment.py +++ b/.codex/skills/beat-saber-mapping/scripts/rl/environment.py @@ -21,6 +21,7 @@ vision_blocking_double, ) from safety_contract import RECOVERY_BEATS, occupied_until +from .safety import ValidationClock, transition_is_safe try: from .rewards import CompositeReward, RewardBreakdown except ImportError: @@ -71,10 +72,14 @@ def __init__( difficulty: str = "Expert", lookahead_steps: int = 4, ): - self.bpm = max(1.0, float(bpm)) + self.bpm = float(bpm) + if not math.isfinite(self.bpm) or self.bpm <= 0: + raise ValueError("RL BPM must be finite and positive") + self.base_bpm = self.bpm self.difficulty = difficulty if difficulty in DIFFICULTIES else "Expert" self.target_nps = TARGET_NPS_MAP.get(self.difficulty, 4.25) self.lookahead_steps = max(1, lookahead_steps) + self.rng = np.random.default_rng(0) self.reward_engine = CompositeReward( difficulty=self.difficulty, @@ -84,9 +89,10 @@ def __init__( # Internal state buffers self.beat_grid = self._normalize_beat_grid(beat_grid) if beat_grid is not None else self._generate_synthetic_grid(64, self.bpm) - self.audio_features = audio_features or self._generate_synthetic_audio(len(self.beat_grid)) + self.audio_features = audio_features if audio_features is not None else self._generate_synthetic_audio(len(self.beat_grid)) self.current_step = 0 self.total_steps = len(self.beat_grid) + self.validation_clock = ValidationClock(self.audio_features, self.beat_grid, self.base_bpm) # Kinematic hand states self.red_prev: Optional[Dict[str, Any]] = None @@ -106,31 +112,25 @@ def _normalize_beat_grid(self, beat_grid: Any) -> List[float]: if beat_grid is None: return self._generate_synthetic_grid(64, self.bpm) if isinstance(beat_grid, dict): - if "beats" in beat_grid and isinstance(beat_grid["beats"], list): - return [float(b) for b in beat_grid["beats"]] - if "tempoRegions" in beat_grid: - max_beat = max((float(r.get("endBeat", 0.0)) for r in beat_grid.get("tempoRegions", [])), default=64.0) - return [round(i * 0.25, 4) for i in range(max(16, int(max_beat * 4)))] - return self._generate_synthetic_grid(64, self.bpm) - return [float(b) for b in beat_grid] + raise ValueError("Use rl.features.load_analysis_features to project an analysis grid") + result = [float(b) for b in beat_grid] + if not result or not np.isfinite(result).all() or np.any(np.diff(result) <= 0): + raise ValueError("Environment beat grid must be nonempty, finite, and strictly increasing") + return result def _generate_synthetic_grid(self, num_beats: int, bpm: float) -> List[float]: # Quarter-beat subdivision return [round(i * 0.25, 4) for i in range(num_beats * 4)] def _generate_synthetic_audio(self, length: int) -> Dict[str, Any]: + stems = ("drums", "bass", "guitar", "piano", "vocals", "other") return { - "onsets": np.random.uniform(0.0, 1.0, size=(length,)).tolist(), - "flux": np.random.uniform(0.0, 1.0, size=(length,)).tolist(), - "stems": { - "drums": np.random.uniform(0.0, 1.0, size=(length,)).tolist(), - "bass": np.random.uniform(0.0, 1.0, size=(length,)).tolist(), - "guitar": np.random.uniform(0.0, 0.5, size=(length,)).tolist(), - "piano": np.random.uniform(0.0, 0.5, size=(length,)).tolist(), - "vocals": np.random.uniform(0.0, 0.8, size=(length,)).tolist(), - "other": np.random.uniform(0.0, 0.5, size=(length,)).tolist(), - }, - "sections": ["verse"] * length, + "onsets": [1.0 if i % 4 == 0 else 0.0 for i in range(length)], + "flux": [0.0] * length, + "stems": {stem: [0.0] * length for stem in stems}, + "stemAvailability": {stem: False for stem in stems}, + "sections": ["unknown"] * length, + "provenance": {"source": "synthetic-test-pulse", "timingStatus": "synthetic"}, } def reset( @@ -142,32 +142,34 @@ def reset( difficulty: Optional[str] = None, ) -> Tuple[np.ndarray, Dict[str, Any]]: if seed is not None: - np.random.seed(seed) + self.rng = np.random.default_rng(seed) if bpm is not None: - self.bpm = max(1.0, float(bpm)) + if not math.isfinite(float(bpm)) or float(bpm) <= 0: + raise ValueError("RL BPM must be finite and positive") + self.bpm = self.base_bpm = float(bpm) if difficulty is not None and difficulty in DIFFICULTIES: self.difficulty = difficulty self.target_nps = TARGET_NPS_MAP.get(self.difficulty, 4.25) - self.reward_engine = CompositeReward( - difficulty=self.difficulty, - bpm=self.bpm, - target_nps=self.target_nps, - ) + self.reward_engine = CompositeReward( + difficulty=self.difficulty, bpm=self.bpm, target_nps=self.target_nps, + ) if beat_grid is not None: self.beat_grid = self._normalize_beat_grid(beat_grid) elif audio_features is not None: - self.beat_grid = self._generate_synthetic_grid(len(audio_features.get("onsets", [])), self.bpm) + self.beat_grid = self._normalize_beat_grid([i * 0.25 for i in range(len(audio_features.get("onsets", [])))]) if audio_features is not None: self.audio_features = audio_features self.current_step = 0 self.total_steps = len(self.beat_grid) + self.validation_clock = ValidationClock(self.audio_features, self.beat_grid, self.base_bpm) self.red_prev = None self.blue_prev = None self.placed_notes = [] + self._update_step_tempo() obs = self._get_observation() info = {"current_step": 0, "beat": self.beat_grid[0] if self.beat_grid else 0.0} @@ -175,10 +177,12 @@ def reset( def action_masks(self) -> Tuple[np.ndarray, np.ndarray]: """Compute binary action masks for Left (Red) and Right (Blue) hands.""" + self._update_step_tempo() mask_red = np.ones(NUM_HAND_ACTIONS, dtype=bool) mask_blue = np.ones(NUM_HAND_ACTIONS, dtype=bool) current_beat = self.beat_grid[self.current_step] if self.current_step < self.total_steps else 0.0 + validation_beat = self.validation_clock.beat(current_beat) recovery_beats = RECOVERY_BEATS.get(self.difficulty, 0.30) # Rhythmic grid subdivision gating @@ -193,7 +197,7 @@ def action_masks(self) -> Tuple[np.ndarray, np.ndarray]: return mask_red, mask_blue # 1. Mask Left Hand (Red, Color 0) - if self.red_prev is not None and (current_beat - float(self.red_prev["b"])) < (recovery_beats - 1e-5): + if self.red_prev is not None and (validation_beat - self.validation_clock.beat(float(self.red_prev["b"]))) < recovery_beats: mask_red[1:] = False else: for act in range(1, NUM_HAND_ACTIONS): @@ -204,14 +208,11 @@ def action_masks(self) -> Tuple[np.ndarray, np.ndarray]: candidate = {"b": current_beat, "c": 0, "x": x, "y": y, "d": d} if self.red_prev is not None: - finding = transition_finding( - self.red_prev, candidate, self.bpm, recovery_beats=recovery_beats - ) - if finding is not None: + if not transition_is_safe(self.validation_clock.note(self.red_prev), self.validation_clock.note(candidate), self.base_bpm, self.difficulty): mask_red[act] = False # 2. Mask Right Hand (Blue, Color 1) - if self.blue_prev is not None and (current_beat - float(self.blue_prev["b"])) < (recovery_beats - 1e-5): + if self.blue_prev is not None and (validation_beat - self.validation_clock.beat(float(self.blue_prev["b"]))) < recovery_beats: mask_blue[1:] = False else: for act in range(1, NUM_HAND_ACTIONS): @@ -222,14 +223,27 @@ def action_masks(self) -> Tuple[np.ndarray, np.ndarray]: candidate = {"b": current_beat, "c": 1, "x": x, "y": y, "d": d} if self.blue_prev is not None: - finding = transition_finding( - self.blue_prev, candidate, self.bpm, recovery_beats=recovery_beats - ) - if finding is not None: + if not transition_is_safe(self.validation_clock.note(self.blue_prev), self.validation_clock.note(candidate), self.base_bpm, self.difficulty): mask_blue[act] = False return mask_red, mask_blue + def blue_action_mask(self, red_action: int, base_mask: Optional[np.ndarray] = None) -> np.ndarray: + """Condition the second hand on the first hand's actual selected action.""" + mask = (base_mask if base_mask is not None else self.action_masks()[1]).copy() + decoded = decode_hand_action(red_action) + if decoded is None or self.current_step >= self.total_steps: + return mask + beat = self.beat_grid[self.current_step] + red = {"b": beat, "c": 0, "x": decoded[0], "y": decoded[1], "d": decoded[2]} + for action in np.flatnonzero(mask[1:]) + 1: + x, y, direction = decode_hand_action(int(action)) + blue = {"b": beat, "c": 1, "x": x, "y": y, "d": direction} + if cross_hand_finding(red, blue) is not None or vision_blocking_double(red, blue): + mask[action] = False + mask[ACTION_IDLE] = True + return mask + def step( self, action: Tuple[int, int] ) -> Tuple[np.ndarray, float, bool, bool, Dict[str, Any]]: @@ -240,6 +254,7 @@ def step( act_red, act_blue = action current_beat = self.beat_grid[self.current_step] + self._update_step_tempo() # Decode actions red_decoded = decode_hand_action(act_red) @@ -269,16 +284,16 @@ def step( # Compute Step Reward breakdown: RewardBreakdown = self.reward_engine.compute_step_reward( - beat=current_beat, - red_prev=self.red_prev, - red_curr=red_curr, - blue_prev=self.blue_prev, - blue_curr=blue_curr, + beat=self.validation_clock.beat(current_beat), + red_prev=self.validation_clock.note(self.red_prev), + red_curr=self.validation_clock.note(red_curr), + blue_prev=self.validation_clock.note(self.blue_prev), + blue_curr=self.validation_clock.note(blue_curr), onset_strength=onset, stem_energy=stem_energy, is_downbeat=is_downbeat, section_type=section_type, - recent_notes=self.placed_notes[-self.history_window:], + recent_notes=[self.validation_clock.note(note) for note in self.placed_notes[-self.history_window:]], current_nps=current_nps, target_nps=self.target_nps, ) @@ -292,6 +307,7 @@ def step( self.blue_prev = blue_curr self.current_step += 1 + self._update_step_tempo() terminated = self.current_step >= self.total_steps truncated = False @@ -310,6 +326,13 @@ def _get_audio_field(self, field: str, idx: int, default: Any) -> Any: return arr[idx] return default + def _update_step_tempo(self) -> None: + if not self.total_steps: + return + value = self._get_audio_field("stepBpms", min(self.current_step, self.total_steps - 1), self.bpm) + self.bpm = float(value) + self.reward_engine.kinematic.bpm = self.base_bpm + def _get_stem_energy(self, stem: str, idx: int) -> float: stems = self.audio_features.get("stems", {}) arr = stems.get(stem) @@ -324,7 +347,11 @@ def _compute_rolling_nps(self) -> float: span_beats = float(recent[-1]["b"]) - float(recent[0]["b"]) if span_beats <= 1e-6: return 0.0 - span_seconds = span_beats * 60.0 / self.bpm + times = self.audio_features.get("timesSeconds") + if times is not None and len(times) == self.total_steps: + span_seconds = float(np.interp(float(recent[-1]["b"]), self.beat_grid, times) - np.interp(float(recent[0]["b"]), self.beat_grid, times)) + else: + span_seconds = span_beats * 60.0 / self.bpm return len(recent) / max(0.1, span_seconds) def _get_observation(self) -> np.ndarray: diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/evaluate.py b/.codex/skills/beat-saber-mapping/scripts/rl/evaluate.py new file mode 100644 index 0000000..e11683f --- /dev/null +++ b/.codex/skills/beat-saber-mapping/scripts/rl/evaluate.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +"""Offline held-out checkpoint evaluation. No downloads, training, or installation.""" + +from __future__ import annotations + +import argparse +from collections import Counter +from datetime import datetime, timezone +import json +import hashlib +from pathlib import Path +from typing import Any + +import numpy as np + +from beatforge_core import ValidationReport +from validate_map import validate_v3 +from timing_export import project_map_timing +from rl.features import AnalysisFeatures, FEATURE_SCHEMA, file_sha256 +from rl.library import load_library + + +def chart_metrics(chart: dict[str, Any], bundle: AnalysisFeatures, difficulty: str) -> dict[str, Any]: + """Report measurable chart properties, without assigning a subjective score.""" + report = ValidationReport() + exported = project_map_timing(chart, bundle.analysis) + structural = validate_v3(exported, f"{difficulty}Standard.dat", report, bpm=bundle.bpm, difficulty=difficulty) + notes = sorted(chart.get("colorNotes", []), key=lambda note: (note["b"], note["c"])) + if not notes: + report.add("error", "EMPTY_CHART", "A playable chart must contain color notes") + duration = bundle.provenance["durationSamples"] / bundle.provenance["sampleRate"] + hand_counts = Counter(int(note["c"]) for note in notes) + transitions = [] + for color in (0, 1): + hand = [note for note in notes if note["c"] == color] + transitions.extend((left["x"], left["y"], left["d"], right["x"], right["y"], right["d"]) + for left, right in zip(hand, hand[1:])) + pattern_counts = Counter(transitions) + labels = bundle.audio_features["sections"] + times = bundle.audio_features["timesSeconds"] + note_onsets = [] + section_counts: Counter[str] = Counter() + for note in notes: + index = int(np.clip(np.searchsorted(bundle.beat_grid, note["b"]), 0, len(labels) - 1)) + section_counts[labels[index]] += 1 + note_onsets.append(bundle.audio_features["onsets"][index]) + section_seconds: Counter[str] = Counter() + for index, label in enumerate(labels): + end = times[index + 1] if index + 1 < len(times) else duration + section_seconds[label] += max(0.0, end - times[index]) + return { + "notes": len(notes), "nps": len(notes) / duration, + "redNotes": hand_counts[0], "blueNotes": hand_counts[1], + "handImbalance": abs(hand_counts[0] - hand_counts[1]) / max(1, len(notes)), + "mostRepeatedTransitionFraction": max(pattern_counts.values(), default=0) / max(1, len(transitions)), + "meanOnsetStrengthAtNotes": float(np.mean(note_onsets)) if note_onsets else 0.0, + "sectionNps": {label: section_counts[label] / seconds for label, seconds in section_seconds.items() if seconds > 0}, + "hardErrors": len(report.errors), "warnings": len(report.warnings), + "errorCodes": dict(Counter(issue.code for issue in report.errors)), + "structural": structural, + "headsetPlaytest": "not_performed", + } + + +def evaluate_checkpoints( + manifest: Path, checkpoints: list[Path], *, split: str = "validation", + difficulty: str = "Expert", seed: int = 0, +) -> dict[str, Any]: + from rl.policy_generator import RLMapGenerator + import torch + + if split not in {"validation", "test"}: + raise ValueError("Evaluation must use a held-out validation or test split") + library = load_library(manifest) + held_out = [(track, bundle) for track, bundle in library if track["split"] == split] + if not held_out: + raise ValueError(f"Manifest has no {split} tracks") + results = [] + for checkpoint in checkpoints: + generator = RLMapGenerator(model_path=Path(checkpoint)) + training = generator.checkpoint_metadata.get("trainingSourceSha256") + if not training: + raise ValueError("Checkpoint has no training audio hashes; held-out independence cannot be verified") + if generator.checkpoint_metadata.get("featureSchema") != FEATURE_SCHEMA: + raise ValueError("Held-out comparison requires the current shared feature schema") + if set(training) & {bundle.provenance["sourceSha256"] for _, bundle in held_out}: + raise ValueError("Held-out split contains audio used to train this checkpoint") + tracks = [] + for track, bundle in held_out: + chart = generator.generate_difficulty(bundle.audio_features, bundle.beat_grid, bundle.bpm, + difficulty=difficulty, deterministic=True, seed=seed) + tracks.append({"id": track["id"], "features": bundle.provenance, + "chartSha256": hashlib.sha256(json.dumps(chart, sort_keys=True).encode()).hexdigest(), + "metrics": chart_metrics(chart, bundle, difficulty)}) + results.append({ + "checkpoint": generator.checkpoint_metadata, "tracks": tracks, + "summary": {"tracks": len(tracks), "hardErrors": sum(t["metrics"]["hardErrors"] for t in tracks), + "meanNps": float(np.mean([t["metrics"]["nps"] for t in tracks])), + "tracksWithoutHardErrors": sum(t["metrics"]["hardErrors"] == 0 for t in tracks)}, + }) + return { + "schemaVersion": 1, "kind": "offline-held-out-checkpoint-evaluation", + "createdAt": datetime.now(timezone.utc).isoformat(), "seed": seed, + "deterministic": True, "split": split, "difficulty": difficulty, + "manifestSha256": file_sha256(manifest), "featureSchema": FEATURE_SCHEMA, + "runtime": {"torch": str(torch.__version__), "numpy": str(np.__version__)}, + "evaluatorSha256": { + name: file_sha256(Path(__file__).parent / name) + for name in ("evaluate.py", "features.py", "environment.py", "policy_generator.py", "models.py", "rewards.py", "safety.py") + } | { + name: file_sha256(Path(__file__).parent.parent / name) + for name in ("validate_map.py", "kinematics.py", "safety_contract.py", "timing_export.py", "beatforge_core.py") + }, + "heldOutVerifiedAgainstRecordedTraining": True, + "limitations": ["Recorded hashes establish data separation; they do not prove prior undocumented training history.", + "Chart validation does not replace a complete packaged-map or headset playtest.", + "Musical preference and manual correction counts require human evaluation."], + "results": results, + } + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--checkpoint", type=Path, action="append", required=True) + parser.add_argument("--split", choices=("validation", "test"), default="validation") + parser.add_argument("--difficulty", choices=("Easy", "Normal", "Hard", "Expert", "ExpertPlus"), default="Expert") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--out", type=Path, required=True) + args = parser.parse_args() + try: + result = evaluate_checkpoints(args.manifest, args.checkpoint, split=args.split, difficulty=args.difficulty, seed=args.seed) + except (ValueError, FileNotFoundError) as exc: + parser.error(str(exc)) + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text(json.dumps(result, indent=2, allow_nan=False) + "\n", encoding="utf-8") + print(f"Saved {len(result['results'])} checkpoint evaluation(s) to {args.out}") + return 2 if any(item["summary"]["hardErrors"] for item in result["results"]) else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/features.py b/.codex/skills/beat-saber-mapping/scripts/rl/features.py new file mode 100644 index 0000000..8b418d7 --- /dev/null +++ b/.codex/skills/beat-saber-mapping/scripts/rl/features.py @@ -0,0 +1,219 @@ +"""One sample-aligned feature contract for RL training, evaluation, and generation. + +Consumes the existing analyzer artifacts. It never estimates or verifies timing. +Missing separated stems are zeros with availability recorded, never mix copies. +""" + +from __future__ import annotations + +from dataclasses import dataclass +import hashlib +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +FEATURE_SCHEMA = "beatforge-rl-sample-grid-v2" +STEMS = ("drums", "bass", "guitar", "piano", "vocals", "other") + + +@dataclass +class AnalysisFeatures: + audio_features: dict[str, Any] + beat_grid: list[float] + bpm: float + provenance: dict[str, Any] + analysis: dict[str, Any] + + +def file_sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _unit(values: np.ndarray) -> np.ndarray: + values = np.maximum(values, 0.0) + scale = max(1.0, float(np.max(values))) if values.size else 1.0 + return np.clip(values / scale, 0.0, 1.0) + + +def build_analysis_features( + analysis: dict[str, Any], + grid_document: dict[str, Any], + sections_document: dict[str, Any], + frames: dict[str, Any], + *, + require_verified: bool = True, +) -> AnalysisFeatures: + """Project frames and onset stem annotations onto actual quarter-beat samples.""" + if not all(isinstance(document, dict) for document in (analysis, grid_document, sections_document, frames)): + raise ValueError("RL analysis artifacts must contain objects") + status = str(analysis.get("status", "unknown")) + if require_verified and status != "timing_verified": + raise ValueError(f"RL requires verified analysis; timing status is {status}") + if grid_document.get("status", status) != status: + raise ValueError("Analysis and beat grid timing statuses disagree") + sample_rate = int(analysis["sampleRate"]) + if float(analysis["sampleRate"]) != sample_rate or float(grid_document.get("sampleRate", sample_rate)) != sample_rate: + raise ValueError("Analysis sample rate must be an integer") + if sample_rate != 44100 or int(grid_document.get("sampleRate", sample_rate)) != sample_rate: + raise ValueError("RL analysis must use the canonical 44100 Hz sample clock") + duration = int(analysis["durationSamples"]) + if float(analysis["durationSamples"]) != duration: + raise ValueError("Analysis durationSamples must be an integer") + bpm = float(analysis["bpm"]) + if duration <= 0 or not math.isfinite(bpm) or bpm <= 0: + raise ValueError("Analysis has invalid duration or BPM") + rows = grid_document.get("beats", []) + if len(rows) < 2 or not all(isinstance(row, dict) for row in rows): + raise ValueError("beat_grid.json must contain at least two beat/sample rows") + beats = np.asarray([row["beat"] for row in rows], dtype=float) + raw_samples = np.asarray([row["sample"] for row in rows], dtype=float) + if (not np.isfinite(beats).all() or not np.isfinite(raw_samples).all() + or np.any(raw_samples != np.round(raw_samples)) + or np.any(np.diff(beats) <= 0) or np.any(np.diff(raw_samples) <= 0)): + raise ValueError("Beat/sample rows must be finite, integer-sampled, and strictly increasing") + if raw_samples[0] < 0 or raw_samples[-1] >= duration: + raise ValueError("Beat samples fall outside the analyzed audio") + if "beatGrid" in analysis: + recorded = [(row["beat"], row["sample"]) for row in analysis["beatGrid"]] + if recorded != [(row["beat"], row["sample"]) for row in rows]: + raise ValueError("Analysis and beat_grid.json contain different adopted timing grids") + + # The analyzer may provide finer subdivisions. Select the quarter-beat domain + # through interpolation in the adopted sample clock, including pickups. + first_tick = math.ceil(beats[0] * 4 - 1e-7) + last_tick = math.floor(beats[-1] * 4 + 1e-7) + steps = np.arange(first_tick, last_tick + 1, dtype=float) / 4.0 + if not len(steps): + raise ValueError("Beat grid contains no quarter-beat decision steps") + samples = np.rint(np.interp(steps, beats, raw_samples)).astype(np.int64) + if np.any(np.diff(samples) <= 0): + raise ValueError("Quarter-beat sample positions must increase") + times = samples / sample_rate + + def frame_values(key: str) -> np.ndarray: + values = np.asarray(frames.get(key, []), dtype=float) + if values.ndim != 1: + raise ValueError(f"Audio feature {key} must be a one-dimensional envelope") + if not np.isfinite(values).all(): + raise ValueError(f"Non-finite audio feature {key}") + return values + + def align(key: str) -> list[float]: + values = frame_values(key) + if not len(values): + return [0.0] * len(steps) + if "samples" in frames: + positions = np.asarray(frames["samples"], dtype=float).reshape(-1) + if np.any(positions != np.round(positions)): + raise ValueError("Audio frame sample positions must be integers") + elif "times" in frames or "timeSeconds" in frames: + positions = np.asarray(frames.get("times", frames.get("timeSeconds")), dtype=float).reshape(-1) * sample_rate + else: + hops = np.asarray(frames.get("hopSamples", frames.get("hop", 0))).reshape(-1) + if len(hops) != 1: + raise ValueError("Audio feature frames require a single hopSamples value") + hop = float(hops[0]) + if not math.isfinite(hop) or hop <= 0 or hop != round(hop): + raise ValueError("Audio feature frames require sample positions, times, or integer hopSamples") + positions = np.arange(len(values)) * hop + if len(positions) != len(values) or not np.isfinite(positions).all() or np.any(np.diff(positions) <= 0): + raise ValueError(f"Invalid frame clock for {key}") + return np.interp(samples, positions, _unit(values), left=0.0, right=0.0).tolist() + + onset_key = next((key for key in ("combinedOnset", "combined", "onsets") if key in frames), None) + if onset_key is None: + raise ValueError("Audio feature artifact is missing its onset envelope") + if not len(frame_values(onset_key)): + raise ValueError("Audio feature artifact contains an empty onset envelope") + onsets = align(onset_key) + flux_available = "flux" in frames and bool(len(frame_values("flux"))) + flux = align("flux") if flux_available else [0.0] * len(steps) + + stems = {stem: [0.0] * len(steps) for stem in STEMS} + available = {stem: False for stem in STEMS} + stem_sources: dict[str, str] = {} + sustains = [0.0] * len(steps) + # Demucs is persisted as event annotations after temporary WAVs are removed. + # Assign an event to its nearest step only; never extend it across silence. + for event in analysis.get("events", []): + energies = event.get("stemEnergy", {}) + sample_value = float(event["sample"]) + if not math.isfinite(sample_value) or sample_value != round(sample_value) or not 0 <= sample_value < duration: + raise ValueError("Analysis onset samples must be integers within the audio") + sample = int(sample_value) + right = min(int(np.searchsorted(samples, sample)), len(samples) - 1) + left = max(0, right - 1) + index = left if abs(int(samples[left]) - sample) <= abs(int(samples[right]) - sample) else right + sustain = float(event.get("sustainBeats", 0.0)) + if not math.isfinite(sustain) or sustain < 0: + raise ValueError("Analysis sustain duration must be finite and nonnegative") + sustains[index] = max(sustains[index], sustain) + for stem in STEMS: + if stem not in energies: + continue + value = float(energies[stem]) + if not math.isfinite(value) or value < 0: + raise ValueError(f"Invalid separated {stem} energy") + available[stem] = True + stem_sources[stem] = "analysis.events.stemEnergy" + stems[stem][index] = max(stems[stem][index], min(value, 1.0)) + + labels = ["unknown"] * len(steps) + for section in sections_document.get("sections", []): + # External segment beat fields may have used a constant BPM conversion. + # Prefer sample/second boundaries to retain offset and tempo changes. + if "startSample" in section and "endSample" in section: + selected = (samples >= int(section["startSample"])) & (samples < int(section["endSample"])) + elif "startSeconds" in section and "endSeconds" in section: + selected = (times >= float(section["startSeconds"])) & (times < float(section["endSeconds"])) + elif "startBeat" in section and "endBeat" in section: + selected = (steps >= float(section["startBeat"])) & (steps < float(section["endBeat"])) + else: + raise ValueError("Section requires a start/end interval") + label = str(section.get("label", section.get("type", "unknown"))) + for index in np.flatnonzero(selected): + labels[int(index)] = label + + intervals = np.searchsorted(beats, steps, side="right") - 1 + intervals = np.clip(intervals, 0, len(beats) - 2) + bpms = 60.0 * sample_rate * np.diff(beats)[intervals] / np.diff(raw_samples)[intervals] + provenance = { + "featureSchema": FEATURE_SCHEMA, "timingStatus": status, + "gridSource": grid_document.get("source", analysis.get("gridSource", "unknown")), + "sourceSha256": analysis.get("sourceSha256"), "sampleRate": sample_rate, + "durationSamples": duration, "decisionSteps": len(steps), + "firstSample": int(samples[0]), "lastSample": int(samples[-1]), + "stemAvailability": available, "stemSources": stem_sources, + "fluxAvailable": flux_available, + "sourceAudioVerified": False, + "sectionSource": sections_document.get("source", "analysis"), + } + audio_features = { + "onsets": onsets, "flux": flux, "stems": stems, "sections": labels, + "sustainBeats": sustains, + "samplePositions": samples.tolist(), "timesSeconds": times.tolist(), + "stepBpms": bpms.tolist(), "stemAvailability": available, + "provenance": provenance, + } + return AnalysisFeatures(audio_features, steps.tolist(), bpm, provenance, {**analysis, "beatGrid": rows}) + + +def load_analysis_features(analysis_dir: Path, *, require_verified: bool = True) -> AnalysisFeatures: + analysis_dir = Path(analysis_dir) + names = ("analysis.json", "beat_grid.json", "sections.json", "audio_features.npz") + for name in names: + if not (analysis_dir / name).is_file(): + raise FileNotFoundError(f"Missing RL analysis artifact: {analysis_dir / name}. Run analyze_audio.py first.") + documents = [json.loads((analysis_dir / name).read_text(encoding="utf-8")) for name in names[:3]] + with np.load(analysis_dir / names[3], allow_pickle=False) as archive: + frames = {key: archive[key] for key in archive.files} + result = build_analysis_features(*documents, frames, require_verified=require_verified) + result.provenance["artifactSha256"] = {name: file_sha256(analysis_dir / name) for name in names} + return result diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/library.py b/.codex/skills/beat-saber-mapping/scripts/rl/library.py new file mode 100644 index 0000000..153b4f1 --- /dev/null +++ b/.codex/skills/beat-saber-mapping/scripts/rl/library.py @@ -0,0 +1,46 @@ +"""Explicit training and held-out song lists, identified by audio content hash.""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +from .features import AnalysisFeatures, file_sha256, load_analysis_features + + +def load_library(manifest: Path) -> list[tuple[dict[str, Any], AnalysisFeatures]]: + manifest = Path(manifest).resolve() + document = json.loads(manifest.read_text(encoding="utf-8")) + if document.get("schemaVersion") != 1 or not isinstance(document.get("tracks"), list): + raise ValueError("Library manifest requires schemaVersion 1 and a tracks list") + result = [] + identities: set[str] = set() + hashes: set[str] = set() + for track in document["tracks"]: + identity = str(track["id"]) + if identity in identities: + raise ValueError(f"Duplicate track ID: {identity}") + if track.get("split") not in {"train", "validation", "test"}: + raise ValueError(f"Track {identity} requires split train, validation, or test") + path = Path(track["analysisDir"]) + path = path if path.is_absolute() else manifest.parent / path + bundle = load_analysis_features(path) + source = bundle.provenance.get("sourceSha256") + if not isinstance(source, str) or len(source) != 64 or any(c not in "0123456789abcdef" for c in source.lower()): + raise ValueError(f"Track {identity} requires an analyzed audio SHA-256") + source = source.lower() + if track.get("audioPath"): + audio = Path(track["audioPath"]) + audio = audio if audio.is_absolute() else manifest.parent / audio + if file_sha256(audio) != source: + raise ValueError(f"Audio content changed since analysis: {identity}") + bundle.provenance["sourceAudioVerified"] = True + if source in hashes: + raise ValueError(f"Duplicate audio content or split leakage: {identity}") + hashes.add(source) + identities.add(identity) + result.append(({**track, "analysisDir": str(path.resolve())}, bundle)) + if not result: + raise ValueError("Library manifest contains no tracks") + return result diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/models.py b/.codex/skills/beat-saber-mapping/scripts/rl/models.py index 187352b..1b17b8e 100644 --- a/.codex/skills/beat-saber-mapping/scripts/rl/models.py +++ b/.codex/skills/beat-saber-mapping/scripts/rl/models.py @@ -3,7 +3,7 @@ from __future__ import annotations -from typing import Dict, List, Optional, Tuple +from typing import Callable, Dict, List, Optional, Tuple import torch import torch.nn as nn @@ -103,6 +103,7 @@ def get_action_and_value( action: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, mask_red: Optional[torch.Tensor] = None, mask_blue: Optional[torch.Tensor] = None, + blue_mask_fn: Optional[Callable[[torch.Tensor], torch.Tensor]] = None, ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]: """Compute action sampling, log probability, entropy, and state value.""" features = self.encoder(obs) @@ -125,6 +126,9 @@ def get_action_and_value( logits_blue = self.actor_blue(blue_in) # Apply Action Masking for Blue Hand + if blue_mask_fn is not None: + conditional_mask = blue_mask_fn(act_red) + mask_blue = conditional_mask if mask_blue is None else mask_blue & conditional_mask if mask_blue is not None: logits_blue = torch.where(mask_blue, logits_blue, torch.tensor(-1e8, device=logits_blue.device)) @@ -147,6 +151,8 @@ def predict( mask_red: Optional[torch.Tensor] = None, mask_blue: Optional[torch.Tensor] = None, deterministic: bool = False, + blue_mask_fn: Optional[Callable[[torch.Tensor], torch.Tensor]] = None, + generator: Optional[torch.Generator] = None, ) -> Tuple[int, int]: """Inference action selection.""" self.eval() @@ -166,19 +172,20 @@ def predict( if deterministic: act_red = torch.argmax(logits_red, dim=-1) else: - dist_red = Categorical(logits=logits_red) - act_red = dist_red.sample() + act_red = torch.multinomial(torch.softmax(logits_red, dim=-1), 1, generator=generator).squeeze(-1) red_emb = self.red_action_embed(act_red) blue_in = torch.cat([features, red_emb], dim=-1) logits_blue = self.actor_blue(blue_in) + if blue_mask_fn is not None: + conditional_mask = blue_mask_fn(act_red) + mask_blue = conditional_mask if mask_blue is None else mask_blue & conditional_mask if mask_blue is not None: logits_blue = torch.where(mask_blue, logits_blue, torch.tensor(-1e8, device=logits_blue.device)) if deterministic: act_blue = torch.argmax(logits_blue, dim=-1) else: - dist_blue = Categorical(logits=logits_blue) - act_blue = dist_blue.sample() + act_blue = torch.multinomial(torch.softmax(logits_blue, dim=-1), 1, generator=generator).squeeze(-1) return int(act_red.item()), int(act_blue.item()) diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/policy_generator.py b/.codex/skills/beat-saber-mapping/scripts/rl/policy_generator.py index ae83cfe..78e9fff 100644 --- a/.codex/skills/beat-saber-mapping/scripts/rl/policy_generator.py +++ b/.codex/skills/beat-saber-mapping/scripts/rl/policy_generator.py @@ -10,6 +10,9 @@ import numpy as np import torch +from .checkpoints import load_checkpoint +from .safety import safe_hold_candidates +from safety_contract import MIN_CHAIN_DURATION_BEATS try: from .environment import ( @@ -40,11 +43,11 @@ def __init__( self.device = device if policy is not None: self.policy = policy - elif model_path is not None and model_path.exists(): - self.policy = ActorCriticPolicy() - self.policy.load_state_dict(torch.load(model_path, map_location=device)) + self.checkpoint_metadata = {"source": "supplied-policy", "checkpointSha256": None} + elif model_path is not None: + self.policy, self.checkpoint_metadata = load_checkpoint(model_path, device) else: - self.policy = ActorCriticPolicy() + raise ValueError("RL generation requires an explicit trained checkpoint (--rl-model)") self.policy.to(device) self.policy.eval() @@ -56,6 +59,7 @@ def generate_difficulty( bpm: float, difficulty: str = "Expert", deterministic: bool = True, + seed: int = 0, ) -> Dict[str, Any]: """Generate a complete v3.3.0 difficulty map using the RL policy.""" env = BeatSaberEnv( @@ -64,7 +68,8 @@ def generate_difficulty( bpm=bpm, difficulty=difficulty, ) - obs, _ = env.reset() + random = torch.Generator(device=self.device).manual_seed(seed) + obs, _ = env.reset(seed=seed) color_notes: List[Dict[str, Any]] = [] bomb_notes: List[Dict[str, Any]] = [] @@ -80,7 +85,11 @@ def generate_difficulty( t_mask_b = torch.tensor(mask_blue, dtype=torch.bool, device=self.device).unsqueeze(0) act_red, act_blue = self.policy.predict( - t_obs, mask_red=t_mask_r, mask_blue=t_mask_b, deterministic=deterministic + t_obs, mask_red=t_mask_r, mask_blue=t_mask_b, deterministic=deterministic, + blue_mask_fn=lambda selected: torch.as_tensor( + env.blue_action_mask(int(selected.item()), mask_blue), dtype=torch.bool, device=self.device + ).unsqueeze(0), + generator=random, ) current_beat = round(beat_grid[step], 4) @@ -116,10 +125,13 @@ def generate_difficulty( break # Generate Arcs (Sliders) connecting smooth swing trajectories (Post-2022 v3 feature) - sliders = self._generate_arcs(color_notes) + sliders = self._generate_arcs(color_notes, audio_features, beat_grid) # Generate Chains (Burst Sliders) on sustained vibration onsets (Post-2022 v3 feature) burst_sliders = self._generate_chains(color_notes, audio_features, beat_grid) + sliders, burst_sliders, hold_relaxations = safe_hold_candidates( + color_notes, sliders, burst_sliders, audio_features, beat_grid, bpm, difficulty + ) # Generate framing walls & bombs obstacles, bomb_notes = self._generate_obstacles_and_bombs( @@ -150,11 +162,16 @@ def generate_difficulty( "_provenance": { "poseSolver": "rl-policy-v1", "format": "v3.3.0", + "seed": seed, + "checkpoint": self.checkpoint_metadata, + "features": audio_features.get("provenance", {}), + "holdRelaxations": hold_relaxations, + "holdSelection": "audio-evidence-with-fixed-note-safety", }, }, } - def _generate_arcs(self, color_notes: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + def _generate_arcs(self, color_notes: List[Dict[str, Any]], audio_features: Dict[str, Any], beat_grid: List[float]) -> List[Dict[str, Any]]: """Generate rich v3 Arcs / Sliders connecting held phrases and melodic transition swings.""" arcs = [] by_color: Dict[int, List[Dict[str, Any]]] = {0: [], 1: []} @@ -166,9 +183,12 @@ def _generate_arcs(self, color_notes: List[Dict[str, Any]]) -> List[Dict[str, An head = notes[i] tail = notes[i + 1] gap = float(tail["b"]) - float(head["b"]) + index = int(np.searchsorted(beat_grid, float(head["b"]))) + sustains = audio_features.get("sustainBeats", []) + sustained = index < len(sustains) and float(sustains[index]) >= gap # Connect notes spaced 0.5 to 3.5 beats apart on melodic transitions - if 0.5 <= gap <= 3.5 and head["d"] != 8 and tail["d"] != 8 and (i % 2 == 0): + if sustained and 0.5 <= gap <= 3.5 and head["d"] != 8 and tail["d"] != 8 and (i % 2 == 0): control = min(2.0, max(0.8, gap * 0.45)) arcs.append({ "b": float(head["b"]), @@ -216,7 +236,7 @@ def _generate_chains( last_chain_beat = -999.0 for i, note in enumerate(color_notes): b = float(note["b"]) - step_idx = min(int(b * 4), len(onsets) - 1) + step_idx = int(np.searchsorted(beat_grid, b)) drum_val = float(drums[step_idx]) if step_idx >= 0 and step_idx < len(drums) and isinstance(drums, list) else 0.0 vocal_val = float(vocals[step_idx]) if step_idx >= 0 and step_idx < len(vocals) and isinstance(vocals, list) else 0.0 @@ -246,8 +266,8 @@ def _generate_chains( ty = min(2, max(0, hy + chosen_dy)) # Determine duration and slice count - dt = 0.0625 if (tx == hx and ty == hy) else (0.125 if abs(chosen_dx) + abs(chosen_dy) <= 1 else 0.25) - sc = 3 if dt <= 0.0625 else (4 if dt <= 0.125 else 5) + dt = MIN_CHAIN_DURATION_BEATS + sc = 5 chains.append({ "b": b, @@ -309,7 +329,7 @@ def notes_in_window(start_b: float, end_b: float) -> List[Dict[str, Any]]: if beat < last_wall_end + min_wall_gap: continue - step_idx = min(int(beat * 4), len(onsets) - 1) + step_idx = i intensity = onsets[step_idx] if 0 <= step_idx < len(onsets) else 0.5 bass_val = bass_energy[step_idx] if 0 <= step_idx < len(bass_energy) else 0.5 diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/registry.py b/.codex/skills/beat-saber-mapping/scripts/rl/registry.py new file mode 100644 index 0000000..2122446 --- /dev/null +++ b/.codex/skills/beat-saber-mapping/scripts/rl/registry.py @@ -0,0 +1,273 @@ +"""Local checkpoint promotion with measured regression gates and rollback. + +Promotion never trains, downloads, installs maps, or asserts human preference. +Stored checkpoints and reports remain immutable by content hash. +""" + +from __future__ import annotations + +import argparse +from contextlib import contextmanager +from datetime import datetime, timezone +import json +import math +import os +from pathlib import Path +import shutil +from typing import Any + +from rl.environment import TARGET_NPS_MAP +from rl.features import FEATURE_SCHEMA, file_sha256 + +PRIMARY_METRIC = "meanOnsetStrengthAtNotes" +MIN_PRIMARY_GAIN = 0.001 + + +def _read(path: Path) -> dict[str, Any]: + return json.loads(path.read_text(encoding="utf-8")) + + +def _finite(metrics: dict[str, Any], key: str) -> float: + value = metrics.get(key) + if isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value): + raise ValueError(f"Evaluation is missing a finite metric: {key}") + return float(value) + + +def _result(report: dict[str, Any], checkpoint_hash: str) -> dict[str, Any]: + if (report.get("kind") != "offline-held-out-checkpoint-evaluation" + or report.get("schemaVersion") != 1 + or report.get("featureSchema") != FEATURE_SCHEMA + or report.get("heldOutVerifiedAgainstRecordedTraining") is not True + or report.get("split") not in {"validation", "test"} + or report.get("deterministic") is not True): + raise ValueError("Promotion requires a verified held-out evaluation with the current schema") + matches = [item for item in report.get("results", []) + if item.get("checkpoint", {}).get("checkpointSha256") == checkpoint_hash] + if len(matches) != 1: + raise ValueError("Evaluation must identify the exact candidate checkpoint once") + selected = matches[0] + training = selected.get("checkpoint", {}).get("trainingSourceSha256") + if not training or not selected.get("tracks"): + raise ValueError("Evaluation must record training hashes and nonempty held-out tracks") + seen: set[str] = set() + for track in selected["tracks"]: + if track["id"] in seen: + raise ValueError("Evaluation repeats a held-out track ID") + seen.add(track["id"]) + features = track.get("features", {}) + source = features.get("sourceSha256") + if not source or source in training or not features.get("artifactSha256"): + raise ValueError("Evaluation contains training overlap or lacks sample artifact identity") + if features.get("timingStatus") != "timing_verified": + raise ValueError("Promotion requires verified timing on every held-out track") + metrics = track["metrics"] + if _finite(metrics, "hardErrors") != 0: + raise ValueError(f"Candidate has hard validation errors on {track['id']}") + if _finite(metrics, "notes") <= 0: + raise ValueError(f"Candidate has an empty chart on {track['id']}") + for key in (PRIMARY_METRIC, "handImbalance", "mostRepeatedTransitionFraction", "nps", "warnings"): + if _finite(metrics, key) < 0: + raise ValueError(f"Metric cannot be negative: {key}") + return selected + + +def compare_evaluations( + baseline: dict[str, Any], baseline_hash: str, + candidate: dict[str, Any], candidate_hash: str, +) -> dict[str, Any]: + """Reject benchmark changes, regressions, and gains below the fixed threshold.""" + before = _result(baseline, baseline_hash) + after = _result(candidate, candidate_hash) + identity = ("manifestSha256", "featureSchema", "split", "difficulty", "seed", "deterministic", "runtime", "evaluatorSha256") + if any(key not in baseline or key not in candidate or baseline[key] != candidate[key] for key in identity): + raise ValueError("Benchmark identity differs; re-evaluate both checkpoints on the same benchmark") + old = {track["id"]: track for track in before["tracks"]} + new = {track["id"]: track for track in after["tracks"]} + if old.keys() != new.keys(): + raise ValueError("Held-out track sets differ") + target = TARGET_NPS_MAP.get(candidate["difficulty"]) + if target is None: + raise ValueError("Unknown evaluation difficulty") + gains = [] + for identity, current in new.items(): + previous = old[identity] + if current["features"] != previous["features"]: + raise ValueError(f"Held-out audio analysis changed: {identity}") + a, b = previous["metrics"], current["metrics"] + for key in ("handImbalance", "mostRepeatedTransitionFraction", "warnings"): + if _finite(b, key) > _finite(a, key) + 1e-9: + raise ValueError(f"Regression in {key} on {identity}") + if abs(_finite(b, "nps") - target) > abs(_finite(a, "nps") - target) + 1e-9: + raise ValueError(f"Regression in target density on {identity}") + gain = _finite(b, PRIMARY_METRIC) - _finite(a, PRIMARY_METRIC) + if gain < -1e-9: + raise ValueError(f"Regression in onset alignment on {identity}") + gains.append(gain) + mean_gain = sum(gains) / len(gains) + if mean_gain < MIN_PRIMARY_GAIN: + raise ValueError(f"Primary metric must improve by at least {MIN_PRIMARY_GAIN}") + return {"primaryMetric": PRIMARY_METRIC, "meanGain": mean_gain, + "minimumGain": MIN_PRIMARY_GAIN, "tracksCompared": len(gains), + "humanPreferenceEstablished": False} + + +@contextmanager +def _locked(root: Path): + root.mkdir(parents=True, exist_ok=True) + lock = root / ".registry.lock" + try: + descriptor = os.open(lock, os.O_CREAT | os.O_EXCL | os.O_WRONLY) + except FileExistsError as exc: + raise ValueError("Registry is locked by another operation; retry after it finishes") from exc + try: + os.close(descriptor) + yield + finally: + lock.unlink() + + +def _save_registry(root: Path, state: dict[str, Any]) -> None: + temporary = root / "registry.json.tmp" + temporary.write_text(json.dumps(state, indent=2, allow_nan=False) + "\n", encoding="utf-8") + temporary.replace(root / "registry.json") + + +def _store_artifact(source: Path, destination: Path, expected: str) -> None: + destination.parent.mkdir(parents=True, exist_ok=True) + if destination.exists(): + if file_sha256(destination) != expected: + raise ValueError("Stored registry artifact failed its content hash") + return + temporary = destination.with_suffix(destination.suffix + ".tmp") + shutil.copyfile(source, temporary) + if file_sha256(temporary) != expected: + temporary.unlink() + raise ValueError("Artifact changed during registry import") + temporary.replace(destination) + + +def register_checkpoint(root: Path, checkpoint: Path, evaluation: Path, *, initialize: bool = False) -> dict[str, Any]: + """Install a baseline explicitly, or promote a measured improvement atomically.""" + from rl.checkpoints import load_checkpoint + + root, checkpoint, evaluation = Path(root), Path(checkpoint), Path(evaluation) + _, metadata = load_checkpoint(checkpoint) + digest = metadata["checkpointSha256"] + report = _read(evaluation) + report_hash = file_sha256(evaluation) + selected = _result(report, digest) + if selected["checkpoint"].get("trainingSourceSha256") != metadata.get("trainingSourceSha256"): + raise ValueError("Evaluation training lineage does not match checkpoint metadata") + with _locked(root): + state = _read(root / "registry.json") if (root / "registry.json").exists() else {"schemaVersion": 1, "active": None, "models": {}, "history": []} + active = state["active"] + if initialize: + if active is not None: + raise ValueError("Registry already has a baseline; use promote") + decision = {"baselineInitialized": True, "humanPreferenceEstablished": False} + else: + if active is None: + raise ValueError("Initialize an evaluated baseline before promotion") + if active == digest: + raise ValueError("Candidate is already the active checkpoint") + previous = state["models"][active] + baseline_path = root / previous["evaluation"] + if file_sha256(baseline_path) != previous["evaluationSha256"]: + raise ValueError("Stored baseline evaluation changed") + decision = compare_evaluations(_read(baseline_path), active, report, digest) + checkpoint_name = f"checkpoints/{digest}.pt" + evaluation_name = f"evaluations/{report_hash}.json" + _store_artifact(checkpoint, root / checkpoint_name, digest) + _store_artifact(evaluation, root / evaluation_name, report_hash) + state["models"][digest] = {"checkpoint": checkpoint_name, "evaluation": evaluation_name, + "evaluationSha256": report_hash, "metadata": metadata} + state["history"].append({"action": "initialize" if initialize else "promote", "from": active, "to": digest, + "at": datetime.now(timezone.utc).isoformat(), "decision": decision}) + state["active"] = digest + _save_registry(root, state) + return {"active": digest, "previous": active, "checkpoint": str((root / checkpoint_name).resolve()), "decision": decision} + + +def active_checkpoint(root: Path) -> Path: + root = Path(root) + state = _read(root / "registry.json") + digest = state.get("active") + if not digest: + raise ValueError("Registry has no active checkpoint") + if not isinstance(digest, str) or len(digest) != 64 or any(c not in "0123456789abcdef" for c in digest): + raise ValueError("Registry contains an invalid checkpoint identity") + path = root / "checkpoints" / f"{digest}.pt" + if file_sha256(path) != digest: + raise ValueError("Active checkpoint failed its recorded content hash") + return path + + +def registry_status(root: Path) -> dict[str, Any]: + """Compact read-only studio status; does not import PyTorch or expose paths.""" + root = Path(root) + if not (root / "registry.json").is_file(): + return {"status": "empty", "active": None, "modelCount": 0, "automaticTraining": False} + try: + state = _read(root / "registry.json") + active_checkpoint(root) + model = state["models"][state["active"]] + metadata = model.get("metadata", {}) + history = state.get("history", []) + return { + "status": "ready", "active": state["active"], "modelCount": len(state["models"]), + "featureSchema": metadata.get("featureSchema"), + "trainingTrackCount": len(metadata.get("trainingSourceSha256", [])), + "lastAction": history[-1] if history else None, + "automaticTraining": False, "humanPreferenceEstablished": False, + } + except (ValueError, KeyError, TypeError, OSError) as exc: + return {"status": "invalid", "active": None, "automaticTraining": False, + "error": "Registry or stored checkpoint integrity check failed"} + + +def rollback(root: Path) -> dict[str, Any]: + root = Path(root) + with _locked(root): + state = _read(root / "registry.json") + active = state["active"] + previous = next((entry["from"] for entry in reversed(state["history"]) + if entry["to"] == active and entry["from"] is not None), None) + if previous is None: + raise ValueError("No previous active checkpoint exists") + path = root / "checkpoints" / f"{previous}.pt" + if file_sha256(path) != previous: + raise ValueError("Rollback checkpoint failed its recorded content hash") + state["active"] = previous + state["history"].append({"action": "rollback", "from": active, "to": previous, + "at": datetime.now(timezone.utc).isoformat()}) + _save_registry(root, state) + return {"active": previous, "previous": active, "checkpoint": str(path.resolve())} + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--registry", required=True, type=Path) + commands = parser.add_subparsers(dest="command", required=True) + for command in ("initialize", "promote"): + sub = commands.add_parser(command) + sub.add_argument("--checkpoint", type=Path, required=True) + sub.add_argument("--evaluation", type=Path, required=True) + commands.add_parser("rollback") + commands.add_parser("active") + args = parser.parse_args() + try: + if args.command == "active": + result = {"checkpoint": str(active_checkpoint(args.registry).resolve())} + elif args.command == "rollback": + result = rollback(args.registry) + else: + result = register_checkpoint(args.registry, args.checkpoint, args.evaluation, initialize=args.command == "initialize") + except (ValueError, FileNotFoundError) as exc: + parser.error(str(exc)) + print(json.dumps(result, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/safety.py b/.codex/skills/beat-saber-mapping/scripts/rl/safety.py new file mode 100644 index 0000000..3cb5d19 --- /dev/null +++ b/.codex/skills/beat-saber-mapping/scripts/rl/safety.py @@ -0,0 +1,86 @@ +"""RL checks in the same constant-BPM coordinates used by the package validator.""" + +from __future__ import annotations + +import math +from collections import Counter +from typing import Any + +import numpy as np + +from kinematics import transition_finding +from safety_contract import MIN_CHAIN_DURATION_BEATS, RECOVERY_BEATS, occupied_until, same_hand_after_hold_too_soon + + +class ValidationClock: + def __init__(self, audio: dict[str, Any], grid: list[float], bpm: float): + self.grid = grid + self.bpm = bpm + times = audio.get("timesSeconds") + if times is None: + self.exported = list(grid) + else: + if len(times) != len(grid) or not np.isfinite(times).all() or np.any(np.diff(times) <= 0): + raise ValueError("RL time positions must match the grid and increase monotonically") + self.exported = [round(float(time) * bpm / 60.0, 8) for time in times] + + def beat(self, beat: float) -> float: + return float(np.interp(beat, self.grid, self.exported)) + + def note(self, note: dict[str, Any] | None) -> dict[str, Any] | None: + return {**note, "b": self.beat(float(note["b"]))} if note is not None else None + + +def transition_is_safe(previous: dict[str, Any], current: dict[str, Any], bpm: float, difficulty: str) -> bool: + if transition_finding(previous, current, bpm, recovery_beats=RECOVERY_BEATS[difficulty]) is not None: + return False + # validate_v3 also enforces this reach cap independently of the swing model. + # Keep validator-based adversarial tests to detect any future contract drift. + gap = float(current["b"]) - float(previous["b"]) + distance = math.hypot(int(current["x"]) - int(previous["x"]), int(current["y"]) - int(previous["y"])) + return not (gap <= .25 and distance > 2.5) + + +def safe_hold_candidates(notes, arcs, chains, audio, grid, bpm, difficulty): + """Keep only decorations compatible with the already selected hand paths. + + The policy currently selects notes, not holds. Decoration must therefore + respect those notes and must never rewrite a valid swing into an unsafe exit. + """ + clock = ValidationClock(audio, grid, bpm) + hands = {color: sorted((clock.note(note) for note in notes if note["c"] == color), key=lambda note: note["b"]) + for color in (0, 1)} + accepted = {"arc": [], "chain": []} + busy = {0: -math.inf, 1: -math.inf} + rejected = Counter() + proposals = [("arc", item) for item in arcs] + [("chain", item) for item in chains] + for kind, item in sorted(proposals, key=lambda proposal: (proposal[1]["b"], proposal[0])): + color = item["c"] + start, tail = clock.beat(item["b"]), clock.beat(item["tb"]) + if item["tb"] > grid[-1] or tail <= start: + rejected["outside_grid"] += 1 + continue + if kind == "chain" and tail - start < MIN_CHAIN_DURATION_BEATS - 1e-8: + rejected["chain_too_short"] += 1 + continue + if start < busy[color]: + rejected["hold_occupancy"] += 1 + continue + hand = hands[color] + inside = [note for note in hand if start + 1e-6 < note["b"] < tail - 1e-6] + if inside or (kind == "chain" and any(abs(note["b"] - tail) <= 1e-6 for note in hand)): + rejected["note_occupancy"] += 1 + continue + next_note = next((note for note in hand if note["b"] > tail + 1e-6), None) + exit_pose = {"b": tail, "c": color, "x": item["tx"], "y": item["ty"], "d": item.get("tc", item["d"])} + if next_note is not None: + gap = next_note["b"] - tail + if same_hand_after_hold_too_soon(gap) or next_note["b"] < occupied_until(tail, RECOVERY_BEATS[difficulty]): + rejected["post_hold_recovery"] += 1 + continue + if not transition_is_safe(exit_pose, next_note, bpm, difficulty): + rejected["tail_exit_motion"] += 1 + continue + accepted[kind].append(item) + busy[color] = occupied_until(tail, RECOVERY_BEATS[difficulty]) + return accepted["arc"], accepted["chain"], dict(rejected) diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/train_custom_tracks.py b/.codex/skills/beat-saber-mapping/scripts/rl/train_custom_tracks.py index ddab251..6a56d98 100644 --- a/.codex/skills/beat-saber-mapping/scripts/rl/train_custom_tracks.py +++ b/.codex/skills/beat-saber-mapping/scripts/rl/train_custom_tracks.py @@ -1,162 +1,122 @@ #!/usr/bin/env python3 -"""Train RL Policy on User Tracks: Daft Punk R.A.M and Ninajirachi.""" +"""Train on verified local analyzer artifacts using the inference feature contract.""" from __future__ import annotations import argparse -import io -import sys from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple +from typing import Any -if hasattr(sys.stdout, "reconfigure"): - sys.stdout.reconfigure(encoding="utf-8", errors="replace") -if hasattr(sys.stderr, "reconfigure"): - sys.stderr.reconfigure(encoding="utf-8", errors="replace") - -import numpy as np import torch -try: - from .environment import BeatSaberEnv - from .models import ActorCriticPolicy - from .train_ppo import train_ppo -except ImportError: - from rl.environment import BeatSaberEnv - from rl.models import ActorCriticPolicy - from rl.train_ppo import train_ppo - -from beatforge_core import AudioBuffer, frame_features, load_audio - - -def extract_track_features(audio_path: Path) -> Tuple[Dict[str, Any], List[float], float]: - """Extract audio features, beat grid, and BPM for RL environment training.""" - print(f"Extracting features from {audio_path.name}...", flush=True) - audio = load_audio(audio_path) - features = frame_features(audio.samples) - hop = int(features["hop"][0]) - sr = audio.sample_rate +from rl.checkpoints import load_checkpoint +from rl.environment import BeatSaberEnv +from rl.features import file_sha256, load_analysis_features +from rl.library import load_library +from rl.models import ActorCriticPolicy +from rl.train_ppo import train_ppo - flux = features["flux"] - bpm_est = 120.0 # default fallback - # Compute rough tempo / beat grid - length = len(flux) - grid_len = max(32, int(audio.duration_samples / (sr * 0.5))) - beat_grid = [round(i * 0.25, 4) for i in range(grid_len)] +def extract_track_features(audio_path: Path, analysis_dir: Path | None = None) -> tuple[dict[str, Any], list[float], float]: + """Load existing analysis; raw audio must match its recorded source hash. - # Interpolate flux into onsets - onsets = np.interp(np.linspace(0, len(flux), grid_len), np.arange(len(flux)), flux) - onsets = (onsets - np.min(onsets)) / (np.max(onsets) - np.min(onsets) + 1e-6) - - # Simulated stem profiles based on transient frequency bands - audio_features = { - "onsets": onsets.tolist(), - "flux": onsets.tolist(), - "stems": { - "drums": (onsets * 0.8 + np.random.uniform(0.0, 0.2, grid_len)).tolist(), - "bass": (onsets * 0.6 + np.random.uniform(0.0, 0.4, grid_len)).tolist(), - "vocals": (onsets * 0.5 + np.random.uniform(0.0, 0.3, grid_len)).tolist(), - "guitar": (onsets * 0.4 + np.random.uniform(0.0, 0.2, grid_len)).tolist(), - "piano": (onsets * 0.3 + np.random.uniform(0.0, 0.2, grid_len)).tolist(), - "other": (onsets * 0.3 + np.random.uniform(0.0, 0.2, grid_len)).tolist(), - }, - "sections": ["verse"] * grid_len, - } - - return audio_features, beat_grid, bpm_est + A directory can be supplied directly. For an audio file, the default artifact + directory is its sibling ``.analysis``. Analysis and human timing review + run before training, so no background model downloads or fake clocks occur. + """ + audio_path = Path(audio_path) + directory = analysis_dir or (audio_path if audio_path.is_dir() else audio_path.with_suffix(".analysis")) + bundle = load_analysis_features(directory) + if audio_path.is_file() and file_sha256(audio_path) != bundle.provenance.get("sourceSha256"): + raise ValueError(f"Analysis does not belong to audio file {audio_path.name}") + if audio_path.is_file(): + bundle.provenance["sourceAudioVerified"] = True + return bundle.audio_features, bundle.beat_grid, bundle.bpm def train_on_custom_library( - tracks: List[Path], + tracks: list[Path], timesteps_per_track: int = 16384, epochs: int = 4, lr: float = 2e-4, model_path: Path = Path("data/models/ppo_policy.pt"), -) -> None: + *, + rounds: int = 1, + seed: int = 0, + resume: bool = True, + held_out_hashes: set[str] | None = None, +) -> dict[str, Any]: + if not tracks or rounds <= 0: + raise ValueError("Training requires tracks and a positive number of rounds") + # Validate the entire library before updating any weights. + prepared = [(Path(track), extract_track_features(Path(track))) for track in tracks] + training_hashes: set[str] = set() + for track, (features, _, _) in prepared: + source = features["provenance"].get("sourceSha256") + if not isinstance(source, str) or len(source) != 64: + raise ValueError(f"Training analysis requires the audio source SHA-256: {track}") + training_hashes.add(source) + held_out_hashes = held_out_hashes or set() + torch.manual_seed(seed) policy = ActorCriticPolicy() - if model_path.is_file(): - try: - policy.load_state_dict(torch.load(model_path, map_location="cpu")) - print(f"Resumed existing weights from {model_path}", flush=True) - except Exception as e: - print(f"Could not load checkpoint: {e}", flush=True) - - print(f"\n=======================================================", flush=True) - print(f" Starting Continuous RL Training on {len(tracks)} User Tracks", flush=True) - print(f"=======================================================\n", flush=True) - - for round_idx in range(1, 100): - print(f"\n--- Training Round {round_idx} Across Library ---", flush=True) - for idx, track_path in enumerate(tracks, 1): - if not track_path.is_file(): - continue - print(f"\n[{idx}/{len(tracks)}] Processing: {track_path.name}", flush=True) - try: - audio_feats, beat_grid, bpm = extract_track_features(track_path) - env = BeatSaberEnv( - audio_features=audio_feats, - beat_grid=beat_grid, - bpm=bpm, - difficulty="Expert", - ) - train_ppo( - policy=policy, - env=env, - total_timesteps=timesteps_per_track, - rollout_steps=512, - ppo_epochs=epochs, - lr=lr, - save_path=model_path, - ) - except Exception as e: - print(f"Error training on {track_path.name}: {e}", flush=True) + prior: dict[str, Any] = {} + if resume and model_path.is_file(): + policy, prior = load_checkpoint(model_path) + if not prior.get("trainingSourceSha256"): + raise ValueError("Checkpoint has no training-library provenance. Use --no-resume for a traceable run.") + training_hashes.update(prior["trainingSourceSha256"]) + if training_hashes & held_out_hashes: + raise ValueError("Held-out audio appears in the checkpoint or requested training library") + + runs = [] + for round_index in range(rounds): + for index, (track, (features, grid, bpm)) in enumerate(prepared): + run_seed = seed + round_index * len(prepared) + index + print(f"Round {round_index + 1}/{rounds}, track {index + 1}/{len(prepared)}: {track.name}", flush=True) + env = BeatSaberEnv(features, grid, bpm, difficulty="Expert") + metrics = train_ppo( + policy, env, total_timesteps=timesteps_per_track, + rollout_steps=min(512, timesteps_per_track), ppo_epochs=epochs, + lr=lr, save_path=model_path, seed=run_seed, + checkpoint_metadata={ + "trainingSourceSha256": sorted(training_hashes), + "heldOutSourceSha256": sorted(held_out_hashes), + "librarySeed": seed, "round": round_index + 1, + "resumedCheckpointSha256": prior.get("checkpointSha256"), + }, + ) + runs.append({"sourceSha256": features["provenance"]["sourceSha256"], **metrics}) + return {"seed": seed, "rounds": rounds, "runs": runs, "checkpoint": str(model_path)} def main() -> int: - parser = argparse.ArgumentParser(description="Train RL policy on specific tracks") - parser.add_argument("--ram-dir", type=Path, default=Path(r"C:\Users\user\Downloads\R.A.M")) - parser.add_argument("--ninajirachi-dir", type=Path, default=Path(r"data\downloads\ninajirachi")) - parser.add_argument("--spotify-dir", type=Path, default=Path(r"data\downloads\spotify_july")) + parser = argparse.ArgumentParser(description=__doc__) + source = parser.add_mutually_exclusive_group(required=True) + source.add_argument("--analysis-dir", action="append", type=Path, help="Repeat for verified analysis directories") + source.add_argument("--manifest", type=Path, help="JSON song library with explicit train/validation/test splits") parser.add_argument("--timesteps-per-track", type=int, default=16384) + parser.add_argument("--rounds", type=int, default=1) + parser.add_argument("--epochs", type=int, default=4) + parser.add_argument("--lr", type=float, default=2e-4) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--resume", action=argparse.BooleanOptionalAction, default=True) parser.add_argument("--out", type=Path, default=Path("data/models/ppo_policy.pt")) args = parser.parse_args() - - tracks: List[Path] = [] - if args.ram_dir.is_dir(): - for ext in ("*.mp3", "*.ogg", "*.wav", "*.m4a"): - tracks.extend(sorted(args.ram_dir.glob(ext))) - - downloads_root = Path("data/downloads") - if downloads_root.is_dir(): - for ext in ("**/*.mp3", "**/*.ogg", "**/*.wav", "**/*.webm", "**/*.m4a"): - tracks.extend(sorted(downloads_root.glob(ext))) - - # Deduplicate paths - unique_tracks = [] - seen = set() - for p in tracks: - res = p.resolve() - if res not in seen and p.is_file(): - seen.add(res) - unique_tracks.append(p) - tracks = unique_tracks - - print(f"Found {len(tracks)} training tracks:") - for t in tracks: - print(f" - {t.name}") - - if not tracks: - print("No audio tracks found!") - return 1 - - train_on_custom_library( - tracks=tracks, - timesteps_per_track=args.timesteps_per_track, - model_path=args.out, - ) + try: + held_out: set[str] = set() + tracks = args.analysis_dir or [] + if args.manifest: + library = load_library(args.manifest) + tracks = [Path(track["analysisDir"]) for track, _ in library if track["split"] == "train"] + held_out = {bundle.provenance["sourceSha256"] for track, bundle in library if track["split"] != "train"} + train_on_custom_library( + tracks, args.timesteps_per_track, args.epochs, args.lr, args.out, + rounds=args.rounds, seed=args.seed, resume=args.resume, held_out_hashes=held_out, + ) + except (ValueError, FileNotFoundError) as exc: + parser.error(str(exc)) return 0 if __name__ == "__main__": - sys.exit(main()) + raise SystemExit(main()) diff --git a/.codex/skills/beat-saber-mapping/scripts/rl/train_ppo.py b/.codex/skills/beat-saber-mapping/scripts/rl/train_ppo.py index 480b6f0..d315d4c 100644 --- a/.codex/skills/beat-saber-mapping/scripts/rl/train_ppo.py +++ b/.codex/skills/beat-saber-mapping/scripts/rl/train_ppo.py @@ -12,6 +12,8 @@ import torch import torch.nn as nn import torch.optim as optim +from rl.checkpoints import load_checkpoint, save_checkpoint +from rl.features import load_analysis_features try: from .environment import BeatSaberEnv, NUM_HAND_ACTIONS @@ -70,12 +72,10 @@ def compute_gae(self, last_value: float, gamma: float = 0.99, gae_lambda: float """Compute Generalized Advantage Estimation (GAE).""" last_gae = 0.0 for t in reversed(range(self.ptr)): - if t == self.ptr - 1: - next_val = last_value - next_done = 0.0 - else: - next_val = self.values[t + 1] - next_done = self.dones[t] + next_val = last_value if t == self.ptr - 1 else self.values[t + 1] + # dones[t] belongs to the transition at t, including the last slot. + # After a terminal step the caller's next observation may be a reset. + next_done = self.dones[t] delta = self.rewards[t] + gamma * next_val * (1.0 - next_done) - self.values[t] last_gae = delta + gamma * gae_lambda * (1.0 - next_done) * last_gae @@ -84,7 +84,8 @@ def compute_gae(self, last_value: float, gamma: float = 0.99, gae_lambda: float self.returns[: self.ptr] = self.advantages[: self.ptr] + self.values[: self.ptr] # Normalize advantages valid_adv = self.advantages[: self.ptr] - self.advantages[: self.ptr] = (valid_adv - valid_adv.mean()) / (valid_adv.std() + 1e-8) + if self.ptr: + self.advantages[: self.ptr] = (valid_adv - valid_adv.mean()) / (valid_adv.std(unbiased=False) + 1e-8) def reset(self) -> None: self.ptr = 0 @@ -105,23 +106,30 @@ def train_ppo( vf_coef: float = 0.5, device: str = "cpu", save_path: Optional[Path] = None, + seed: int = 0, + checkpoint_metadata: Optional[Dict[str, Any]] = None, ) -> Dict[str, float]: + if min(total_timesteps, rollout_steps, ppo_epochs, batch_size) <= 0 or lr <= 0: + raise ValueError("Training steps, epochs, batch size, and learning rate must be positive") + torch.manual_seed(seed) + rng = np.random.default_rng(seed) policy.to(device) optimizer = optim.Adam(policy.parameters(), lr=lr, eps=1e-5) buffer = RolloutBuffer(buffer_size=rollout_steps, obs_dim=env.obs_dim, device=device) - obs, _ = env.reset() + obs, _ = env.reset(seed=seed) timesteps_done = 0 iteration = 0 history: Dict[str, List[float]] = {"ep_return": [], "policy_loss": [], "value_loss": []} + current_ep_reward = 0.0 + completed_returns: List[float] = [] while timesteps_done < total_timesteps: buffer.reset() ep_rewards = [] - current_ep_reward = 0.0 policy.eval() - for step in range(rollout_steps): + for step in range(min(rollout_steps, total_timesteps - timesteps_done)): mask_red, mask_blue = env.action_masks() t_obs = torch.tensor(obs, dtype=torch.float32, device=device).unsqueeze(0) t_mask_r = torch.tensor(mask_red, dtype=torch.bool, device=device).unsqueeze(0) @@ -129,15 +137,28 @@ def train_ppo( with torch.no_grad(): (act_red, act_blue), log_prob, _, val = policy.get_action_and_value( - t_obs, mask_red=t_mask_r, mask_blue=t_mask_b + t_obs, mask_red=t_mask_r, mask_blue=t_mask_b, + blue_mask_fn=lambda selected: torch.as_tensor( + env.blue_action_mask(int(selected.item()), mask_blue), dtype=torch.bool, device=device + ).unsqueeze(0), ) a_r = int(act_red.item()) a_b = int(act_blue.item()) + # Replay must use the same conditional distribution as collection. + mask_blue = env.blue_action_mask(a_r, mask_blue) next_obs, reward, terminated, truncated, info = env.step((a_r, a_b)) current_ep_reward += reward + # A time limit is not an MDP terminal. Bootstrap its final observation, + # then cut the advantage trace before the reset episode. + if truncated and not terminated: + with torch.no_grad(): + final_obs = torch.as_tensor(next_obs, dtype=torch.float32, device=device).unsqueeze(0) + final_value = policy.forward(final_obs)[2].item() + reward += gamma * float(final_value) + buffer.add( obs=obs, act_red=a_r, @@ -155,12 +176,13 @@ def train_ppo( if terminated or truncated: ep_rewards.append(current_ep_reward) + completed_returns.append(current_ep_reward) current_ep_reward = 0.0 obs, _ = env.reset() with torch.no_grad(): t_obs = torch.tensor(obs, dtype=torch.float32, device=device).unsqueeze(0) - _, _, _, last_val = policy.get_action_and_value(t_obs) + _, _, last_val = policy.forward(t_obs) last_value = float(last_val.item()) buffer.compute_gae(last_value=last_value, gamma=gamma, gae_lambda=gae_lambda) @@ -173,7 +195,7 @@ def train_ppo( indices = np.arange(buffer.ptr) for epoch in range(ppo_epochs): - np.random.shuffle(indices) + rng.shuffle(indices) for start in range(0, len(indices), batch_size): end = start + batch_size mb_idx = indices[start:end] @@ -229,11 +251,21 @@ def train_ppo( if save_path is not None: save_path.parent.mkdir(parents=True, exist_ok=True) - torch.save(policy.state_dict(), save_path) + metadata = dict(checkpoint_metadata or {}) + source = env.audio_features.get("provenance", {}).get("sourceSha256") + if source: + metadata["trainingSourceSha256"] = sorted(set(metadata.get("trainingSourceSha256", [])) | {source}) + save_checkpoint(policy, save_path, { + **metadata, "algorithm": "ppo", "seed": seed, + "lastRunTimesteps": timesteps_done, + "features": env.audio_features.get("provenance", {}), + }) print(f"Saved trained PPO policy to {save_path}", flush=True) return { - "final_mean_reward": float(np.mean(ep_rewards) if ep_rewards else current_ep_reward), + "final_mean_reward": float(np.mean(completed_returns) if completed_returns else current_ep_reward), + "timesteps": timesteps_done, + "completed_episodes": len(completed_returns), "policy_loss": avg_p_loss, "value_loss": avg_v_loss, } @@ -244,18 +276,28 @@ def main() -> int: parser.add_argument("--timesteps", type=int, default=1024) parser.add_argument("--rollout-steps", type=int, default=256) parser.add_argument("--epochs", type=int, default=4) - parser.add_argument("--resume", action="store_true", default=True, help="Resume from existing checkpoint if available") + parser.add_argument("--resume", action=argparse.BooleanOptionalAction, default=True) + parser.add_argument("--lr", type=float, default=3e-4) + parser.add_argument("--seed", type=int, default=0) + source = parser.add_mutually_exclusive_group(required=True) + source.add_argument("--analysis-dir", type=Path, help="Verified analyzer artifact directory") + source.add_argument("--synthetic", action="store_true", help="Explicit smoke-test pulse, not real-song training") parser.add_argument("--out", type=Path, default=Path("data/models/ppo_policy.pt")) args = parser.parse_args() - env = BeatSaberEnv(difficulty="Expert") + torch.manual_seed(args.seed) + if args.analysis_dir: + bundle = load_analysis_features(args.analysis_dir) + env = BeatSaberEnv(bundle.audio_features, bundle.beat_grid, bundle.bpm, difficulty="Expert") + else: + env = BeatSaberEnv(difficulty="Expert") policy = ActorCriticPolicy() + prior_metadata = {} if args.resume and args.out.is_file(): - try: - policy.load_state_dict(torch.load(args.out, map_location="cpu")) - print(f"Resumed policy weights from {args.out}", flush=True) - except Exception as e: - print(f"Could not load checkpoint: {e}, starting from scratch", flush=True) + policy, prior_metadata = load_checkpoint(args.out) + if args.analysis_dir and not prior_metadata.get("trainingSourceSha256"): + parser.error("Checkpoint training lineage is unknown. Use --no-resume to start a traceable run.") + print(f"Resumed policy weights from {args.out}", flush=True) train_ppo( policy=policy, @@ -265,6 +307,8 @@ def main() -> int: ppo_epochs=args.epochs, lr=args.lr, save_path=args.out, + seed=args.seed, + checkpoint_metadata=prior_metadata, ) return 0 diff --git a/.codex/skills/beat-saber-mapping/scripts/timing_export.py b/.codex/skills/beat-saber-mapping/scripts/timing_export.py new file mode 100644 index 0000000..c0c180d --- /dev/null +++ b/.codex/skills/beat-saber-mapping/scripts/timing_export.py @@ -0,0 +1,83 @@ +"""Project authored musical beats into the constant BPM clock used by exported maps.""" + +from __future__ import annotations + +from bisect import bisect_right +from copy import deepcopy +import math +from typing import Any + + +class ExportClock: + def __init__(self, analysis: dict[str, Any]): + self.bpm = float(analysis["bpm"]) + self.sample_rate = int(analysis.get("sampleRate", 44100)) + rows = analysis.get("beatGrid", []) + if len(rows) < 2: + raise ValueError("Export timing requires at least two adopted beat/sample rows") + self.beats = [float(row["beat"]) for row in rows] + self.samples = [float(row["sample"]) for row in rows] + if not math.isfinite(self.bpm) or self.bpm <= 0 or self.sample_rate <= 0: + raise ValueError("Invalid export BPM or sample rate") + if not all(math.isfinite(value) for value in self.beats + self.samples): + raise ValueError("Export timing grid contains a non-finite position") + if any(right <= left for values in (self.beats, self.samples) for left, right in zip(values, values[1:])): + raise ValueError("Export timing grid must increase in beat and sample positions") + + def beat(self, authored: float) -> float: + index = min(len(self.beats) - 2, max(0, bisect_right(self.beats, authored) - 1)) + ratio = (authored - self.beats[index]) / (self.beats[index + 1] - self.beats[index]) + sample = self.samples[index] + ratio * (self.samples[index + 1] - self.samples[index]) + # Round to the source sample clock before writing fractional export beats. + return round(round(sample) / self.sample_rate * self.bpm / 60.0, 8) + + +def project_map_timing(beatmap: dict[str, Any], analysis: dict[str, Any]) -> dict[str, Any]: + """Keep absolute and relative generated v3 event times on the analyzed sample clock.""" + clock = ExportClock(analysis) + result = deepcopy(beatmap) + if result.get("customData", {}).get("_beatforgeTiming"): + raise ValueError("Map timing has already been projected") + if result.get("bpmEvents"): + raise ValueError("Authored maps with BPM events require an explicit BPM-event conversion") + for key in ("colorNotes", "bombNotes", "sliders", "burstSliders", "obstacles", "rotationEvents", "basicBeatmapEvents", "colorBoostBeatmapEvents"): + for item in result.get(key, []): + start = float(item["b"]) + item["b"] = clock.beat(start) + if key in {"sliders", "burstSliders"}: + item["tb"] = clock.beat(float(item["tb"])) + elif key == "obstacles": + item["d"] = round(clock.beat(start + float(item["d"])) - item["b"], 8) + if key in {"basicBeatmapEvents", "colorBoostBeatmapEvents"}: + item["b"] = max(0.0, item["b"]) + for key in ("lightColorEventBoxGroups", "lightRotationEventBoxGroups", "lightTranslationEventBoxGroups"): + for group in result.get(key, []): + start = float(group["b"]) + exported_start = clock.beat(start) + group["b"] = max(0.0, exported_start) + for box in group.get("e", []): + # Only event arrays contain relative beat times. The filter's b is a flag. + for array in ("e", "l", "t"): + for event in box.get(array, []): + if "b" in event: + event["b"] = round(clock.beat(start + float(event["b"])) - exported_start, 8) + result["bpmEvents"] = [] + result.setdefault("customData", {})["_beatforgeTiming"] = { + "schemaVersion": 1, "clock": "constant-bpm-sample-projection", + "sampleRate": clock.sample_rate, "bpm": clock.bpm, + } + return result + + +def project_plan_timing(plan: dict[str, Any], analysis: dict[str, Any]) -> dict[str, Any]: + clock = ExportClock(analysis) + result = deepcopy(plan) + for section in result.get("sections", []): + for key in ("startBeat", "endBeat"): + if key in section: + section["authored" + key[0].upper() + key[1:]] = section[key] + section[key] = clock.beat(float(section[key])) + section["startSeconds"] = float(section["startBeat"]) * 60.0 / clock.bpm + section["endSeconds"] = float(section["endBeat"]) * 60.0 / clock.bpm + result["exportClock"] = "constant-bpm-sample-projection" + return result diff --git a/.codex/skills/beat-saber-mapping/tests/test_rl_environment.py b/.codex/skills/beat-saber-mapping/tests/test_rl_environment.py index 91d5d32..4822f82 100644 --- a/.codex/skills/beat-saber-mapping/tests/test_rl_environment.py +++ b/.codex/skills/beat-saber-mapping/tests/test_rl_environment.py @@ -179,7 +179,7 @@ def test_ppo_training_loop(): def test_rl_map_generator_v3(): - generator = RLMapGenerator() + generator = RLMapGenerator(policy=ActorCriticPolicy()) bpm = 128.0 grid = [round(i * 0.25, 4) for i in range(32 * 4)] # 32 beats audio = { diff --git a/.codex/skills/beat-saber-mapping/tests/test_rl_features.py b/.codex/skills/beat-saber-mapping/tests/test_rl_features.py new file mode 100644 index 0000000..b795c91 --- /dev/null +++ b/.codex/skills/beat-saber-mapping/tests/test_rl_features.py @@ -0,0 +1,115 @@ +"""Regression checks for real analysis clocks and truthful RL inputs.""" + +import json + +import numpy as np +import pytest + +from rl.features import build_analysis_features, load_analysis_features, STEMS +from rl.library import load_library + + +def documents(): + # Offset is half a second; tempo changes from 120 to 60 at beat 1. + rows = [{"beat": beat, "sample": sample} for beat, sample in + [(0, 22050), (.5, 33075), (1, 44100), (1.5, 66150), (2, 88200)]] + analysis = {"status": "timing_verified", "sampleRate": 44100, "durationSamples": 110250, + "bpm": 120, "sourceSha256": "a" * 64, + "events": [{"sample": 44100, "stemEnergy": {"drums": .8, "bass": .2}}]} + grid = {"sampleRate": 44100, "status": "timing_verified", "beats": rows, "source": "confirmed"} + sections = {"source": "all-in-one", "sections": [ + {"label": "verse", "startSeconds": 0, "endSeconds": 1, "startBeat": 0, "endBeat": 99}, + {"label": "chorus", "startSeconds": 1, "endSeconds": 2.5, "startBeat": 99, "endBeat": 100}, + ]} + frames = {"times": np.arange(11) * .25, "combinedOnset": np.arange(11) / 10, + "flux": np.linspace(1, 0, 11)} + return analysis, grid, sections, frames + + +def write_analysis(path, *, source="a" * 64): + path.mkdir(parents=True, exist_ok=True) + analysis, grid, sections, frames = documents() + analysis["sourceSha256"] = source + for name, doc in [("analysis.json", analysis), ("beat_grid.json", grid), ("sections.json", sections)]: + (path / name).write_text(json.dumps(doc), encoding="utf-8") + np.savez_compressed(path / "audio_features.npz", **frames) + return path + + +def test_offset_and_tempo_changes_use_actual_sample_positions(): + result = build_analysis_features(*documents()) + assert result.beat_grid == [i / 4 for i in range(9)] + assert result.audio_features["samplePositions"] == [22050, 27562, 33075, 38588, 44100, 55125, 66150, 77175, 88200] + assert result.audio_features["onsets"][0] == pytest.approx(.2) + assert result.audio_features["onsets"][-1] == pytest.approx(.8) + assert result.audio_features["stepBpms"][:4] == pytest.approx([120] * 4) + assert result.audio_features["stepBpms"][4:] == pytest.approx([60] * 5) + assert result.provenance["durationSamples"] == 110250 + + +def test_sections_use_seconds_over_stale_constant_tempo_beat_fields(): + result = build_analysis_features(*documents()) + assert result.audio_features["sections"] == ["verse"] * 4 + ["chorus"] * 5 + + +def test_stems_are_real_localized_annotations_and_missing_stems_are_honest(): + result = build_analysis_features(*documents()) + audio = result.audio_features + assert audio["stems"]["drums"] == [0, 0, 0, 0, .8, 0, 0, 0, 0] + assert audio["stemAvailability"]["drums"] is True + assert audio["stemAvailability"]["vocals"] is False + assert audio["stems"]["vocals"] == [0] * 9 + analysis, grid, sections, frames = documents() + analysis["events"] = [] + missing = build_analysis_features(analysis, grid, sections, frames) + assert all(not missing.audio_features["stemAvailability"][stem] for stem in STEMS) + assert all(not any(values) for values in missing.audio_features["stems"].values()) + + +def test_hop_clock_does_not_stretch_frames_to_grid_length(): + analysis, grid, sections, _ = documents() + frames = {"hopSamples": 11025, "combinedOnset": np.arange(11) / 10} + result = build_analysis_features(analysis, grid, sections, frames) + assert result.audio_features["onsets"][0] == pytest.approx(.2) + assert result.audio_features["onsets"][-1] == pytest.approx(.8) + assert result.audio_features["flux"] == [0] * 9 + assert result.provenance["fluxAvailable"] is False + + +@pytest.mark.parametrize("bad", [float("nan"), 33074.5, 22050]) +def test_invalid_sample_grid_fails(bad): + args = list(documents()) + args[1]["beats"][1]["sample"] = bad + with pytest.raises(ValueError, match="strictly increasing"): + build_analysis_features(*args) + + +def test_unconfirmed_timing_requires_explicit_generation_override(): + analysis, grid, sections, frames = documents() + analysis["status"] = grid["status"] = "needs_anchors" + with pytest.raises(ValueError, match="verified"): + build_analysis_features(analysis, grid, sections, frames) + allowed = build_analysis_features(analysis, grid, sections, frames, require_verified=False) + assert allowed.provenance["timingStatus"] == "needs_anchors" + + +def test_artifact_hashes_and_repeatability(tmp_path): + path = write_analysis(tmp_path / "analysis") + first = load_analysis_features(path) + assert first == load_analysis_features(path) + assert len(first.provenance["artifactSha256"]) == 4 + (path / "audio_features.npz").unlink() + with pytest.raises(FileNotFoundError, match="Run analyze_audio"): + load_analysis_features(path) + + +def test_library_rejects_content_overlap_even_if_track_names_differ(tmp_path): + write_analysis(tmp_path / "one") + write_analysis(tmp_path / "two") + manifest = tmp_path / "library.json" + manifest.write_text(json.dumps({"schemaVersion": 1, "tracks": [ + {"id": "first", "analysisDir": "one", "split": "train"}, + {"id": "different-name", "analysisDir": "two", "split": "validation"}, + ]})) + with pytest.raises(ValueError, match="split leakage"): + load_library(manifest) diff --git a/.codex/skills/beat-saber-mapping/tests/test_rl_invariants.py b/.codex/skills/beat-saber-mapping/tests/test_rl_invariants.py new file mode 100644 index 0000000..2ef21c1 --- /dev/null +++ b/.codex/skills/beat-saber-mapping/tests/test_rl_invariants.py @@ -0,0 +1,113 @@ +"""Independent serialization, episode-boundary, and reproducibility regressions.""" + +from collections import Counter + +import numpy as np +import pytest + +torch = pytest.importorskip("torch") + +from beatforge_core import ValidationReport +from rl.checkpoints import save_checkpoint +from rl.environment import BeatSaberEnv +from rl.models import ActorCriticPolicy +from rl.policy_generator import RLMapGenerator +from rl.safety import safe_hold_candidates +from rl.train_ppo import RolloutBuffer, train_ppo +from timing_export import project_map_timing +from validate_map import validate_v3 + + +def fixture_audio(length=96, bpm=160, variable=False): + grid = [index / 4 for index in range(length)] + tempos = [90 if index < length // 2 else 220 for index in range(length)] if variable else [bpm] * length + samples = [11025] + for index in range(length - 1): + samples.append(samples[-1] + round(44100 * 60 / tempos[index] / 4)) + audio = { + "onsets": [.95] * length, "flux": [.8] * length, "sustainBeats": [2.] * length, + "sections": ["chorus"] * length, + "stems": {name: [.9] * length for name in ("drums", "bass", "guitar", "piano", "vocals", "other")}, + "timesSeconds": [sample / 44100 for sample in samples], "stepBpms": tempos, + } + analysis = {"bpm": bpm, "sampleRate": 44100, + "beatGrid": [{"beat": beat, "sample": sample} for beat, sample in zip(grid, samples)]} + return audio, grid, analysis + + +@pytest.mark.parametrize("difficulty", ["Easy", "Hard", "ExpertPlus"]) +@pytest.mark.parametrize("bpm,variable", [(90, False), (220, False), (120, True)]) +@pytest.mark.parametrize("seed", [0, 3]) +def test_dense_adversarial_rl_output_passes_independent_exported_validator(difficulty, bpm, variable, seed): + torch.manual_seed(seed) + audio, grid, analysis = fixture_audio(bpm=bpm, variable=variable) + chart = RLMapGenerator(policy=ActorCriticPolicy(hidden_dim=16)).generate_difficulty( + audio, grid, bpm, difficulty=difficulty, deterministic=False, seed=seed, + ) + report = ValidationReport() + validate_v3(project_map_timing(chart, analysis), "fixture.dat", report, bpm=bpm, difficulty=difficulty) + assert len(chart["colorNotes"]) > len(grid) // 8, "Passing by emitting an empty or nearly empty chart is not acceptable" + assert not report.errors, dict(Counter(issue.code for issue in report.errors)) + + +@pytest.mark.parametrize("deterministic", [True, False]) +def test_checkpoint_generation_is_reproducible_and_preserves_caller_rng(tmp_path, deterministic): + torch.manual_seed(17) + checkpoint = tmp_path / "policy.pt" + save_checkpoint(ActorCriticPolicy(hidden_dim=16), checkpoint, {"trainingSourceSha256": ["a" * 64]}) + audio, grid, _ = fixture_audio(length=40) + state = torch.random.get_rng_state().clone() + first = RLMapGenerator(model_path=checkpoint).generate_difficulty(audio, grid, 160, deterministic=deterministic, seed=42) + assert torch.equal(state, torch.random.get_rng_state()) + torch.rand(13) # Unrelated application use must not change the map's random stream. + second = RLMapGenerator(model_path=checkpoint).generate_difficulty(audio, grid, 160, deterministic=deterministic, seed=42) + assert first == second + if not deterministic: + other = RLMapGenerator(model_path=checkpoint).generate_difficulty(audio, grid, 160, deterministic=False, seed=43) + assert other["colorNotes"] != first["colorNotes"] + + +def test_safe_holds_survive_without_erasing_notes_or_overlapping_ownership(): + notes = [{"b": beat, "c": 0, "x": 1, "y": 0, "d": direction} for beat, direction in [(0, 1), (1, 0), (2, 1), (4, 0)]] + arc = {"b": 0, "c": 0, "x": 1, "y": 0, "d": 1, "tb": 1, "tx": 1, "ty": 0, "tc": 0, "mu": 1, "tmu": 1, "m": 0} + chain = {"b": 2, "c": 0, "x": 1, "y": 0, "d": 1, "tb": 2.25, "tx": 1, "ty": 0, "sc": 5, "s": 1} + conflicting = {**chain, "b": 0, "tb": .25} + grid = [index / 4 for index in range(17)] + arcs, chains, rejected = safe_hold_candidates(notes, [arc], [chain, conflicting], {}, grid, 120, "Easy") + assert arcs == [arc] and chains == [chain] + assert rejected["hold_occupancy"] == 1 + report = ValidationReport() + validate_v3({"version": "3.3.0", "colorNotes": notes, "sliders": arcs, "burstSliders": chains}, + "fixture.dat", report, bpm=120, difficulty="Easy") + assert not report.errors + assert len(notes) == 4 + + +@pytest.mark.parametrize("terminal,expected", [(True, 2.), (False, 8.3)]) +def test_actual_ppo_rollout_distinguishes_terminal_and_time_limit(monkeypatch, terminal, expected): + class BoundaryEnv(BeatSaberEnv): + def reset(self, **kwargs): + super().reset(**kwargs) + return np.zeros(self.obs_dim, dtype=np.float32), {} + + def step(self, action): + return np.ones(self.obs_dim, dtype=np.float32), 2., terminal, not terminal, {} + + policy = ActorCriticPolicy(hidden_dim=16) + # Final observation has value 7; reset observation has deliberately huge value + # 123. Neither a true terminal nor the new episode may bootstrap from 123. + monkeypatch.setattr(policy, "forward", lambda obs: (None, None, torch.where(obs[:, 0] > 0, 7., 123.))) + recorded = {} + original = RolloutBuffer.compute_gae + + def inspect(self, *args, **kwargs): + original(self, *args, **kwargs) + recorded["return"] = self.returns[0].item() + recorded["reward"] = self.rewards[0].item() + recorded["done"] = self.dones[0].item() + + monkeypatch.setattr(RolloutBuffer, "compute_gae", inspect) + result = train_ppo(policy, BoundaryEnv(beat_grid=[0, .25]), total_timesteps=1, + rollout_steps=1, ppo_epochs=1, batch_size=1, gamma=.9, seed=4) + assert recorded == pytest.approx({"return": expected, "reward": expected, "done": 1.}) + assert result["final_mean_reward"] == 2., "Reported episode reward must exclude the bootstrap value" diff --git a/.codex/skills/beat-saber-mapping/tests/test_rl_registry.py b/.codex/skills/beat-saber-mapping/tests/test_rl_registry.py new file mode 100644 index 0000000..6171bae --- /dev/null +++ b/.codex/skills/beat-saber-mapping/tests/test_rl_registry.py @@ -0,0 +1,122 @@ +"""Promotion requires the same benchmark, measured progress, and no regressions.""" + +import copy +import json + +import pytest + +from rl.features import FEATURE_SCHEMA +from rl.registry import active_checkpoint, compare_evaluations, register_checkpoint, rollback, registry_status + + +def evaluation(checkpoint_hash, onset=.5): + return { + "schemaVersion": 1, "kind": "offline-held-out-checkpoint-evaluation", + "featureSchema": FEATURE_SCHEMA, "heldOutVerifiedAgainstRecordedTraining": True, + "split": "validation", "difficulty": "Expert", "seed": 0, "deterministic": True, + "manifestSha256": "d" * 64, "runtime": {"torch": "test", "numpy": "test"}, + "evaluatorSha256": {"evaluate.py": "e" * 64}, + "results": [{"checkpoint": {"checkpointSha256": checkpoint_hash, "trainingSourceSha256": ["a" * 64]}, + "tracks": [{"id": "held-out", "features": { + "sourceSha256": "b" * 64, "timingStatus": "timing_verified", + "artifactSha256": {"beat_grid.json": "c" * 64}, + }, "metrics": {"hardErrors": 0, "notes": 100, "nps": 4.25, + "handImbalance": .1, "mostRepeatedTransitionFraction": .1, + "warnings": 0, "meanOnsetStrengthAtNotes": onset}}]}], + } + + +def test_promotion_requires_primary_gain_and_preserves_other_metrics(): + result = compare_evaluations(evaluation("before"), "before", evaluation("after", .6), "after") + assert result["meanGain"] == pytest.approx(.1) + assert result["humanPreferenceEstablished"] is False + + +@pytest.mark.parametrize("key,value", [ + ("hardErrors", 1), ("notes", 0), ("handImbalance", .2), + ("mostRepeatedTransitionFraction", .3), ("warnings", 1), ("nps", 7), + ("meanOnsetStrengthAtNotes", .5), ("meanOnsetStrengthAtNotes", float("nan")), +]) +def test_regression_or_no_improvement_cannot_promote(key, value): + candidate = evaluation("after", .7) + candidate["results"][0]["tracks"][0]["metrics"][key] = value + with pytest.raises(ValueError): + compare_evaluations(evaluation("before"), "before", candidate, "after") + + +@pytest.mark.parametrize("field,value", [("manifestSha256", "different"), ("seed", 4), + ("difficulty", "Hard"), ("evaluatorSha256", {})]) +def test_changed_benchmark_cannot_promote(field, value): + candidate = evaluation("after", .7) + candidate[field] = value + with pytest.raises(ValueError, match="Benchmark identity"): + compare_evaluations(evaluation("before"), "before", candidate, "after") + + +def test_one_improved_track_cannot_hide_a_regression_elsewhere(): + baseline = evaluation("before") + second = copy.deepcopy(baseline["results"][0]["tracks"][0]) + second["id"] = "second" + second["features"]["sourceSha256"] = "f" * 64 + baseline["results"][0]["tracks"].append(second) + candidate = copy.deepcopy(baseline) + candidate["results"][0]["checkpoint"]["checkpointSha256"] = "after" + candidate["results"][0]["tracks"][0]["metrics"]["meanOnsetStrengthAtNotes"] = 1 + candidate["results"][0]["tracks"][1]["metrics"]["meanOnsetStrengthAtNotes"] = .4 + with pytest.raises(ValueError, match="Regression in onset alignment"): + compare_evaluations(baseline, "before", candidate, "after") + + +def test_registry_promotion_and_rollback_preserve_checkpoint_files(tmp_path): + torch = pytest.importorskip("torch") + from rl.checkpoints import load_checkpoint, save_checkpoint + from rl.models import ActorCriticPolicy + paths = [] + for seed in (1, 2, 3): + torch.manual_seed(seed) + path = tmp_path / f"policy-{seed}.pt" + save_checkpoint(ActorCriticPolicy(hidden_dim=16), path, {"trainingSourceSha256": ["a" * 64]}) + _, metadata = load_checkpoint(path) + report_path = tmp_path / f"report-{seed}.json" + report_path.write_text(json.dumps(evaluation(metadata["checkpointSha256"], .4 + seed / 10))) + paths.append((path, report_path)) + registry = tmp_path / "registry" + baseline = register_checkpoint(registry, *paths[0], initialize=True) + previous_bytes = active_checkpoint(registry).read_bytes() + promoted = register_checkpoint(registry, *paths[1]) + assert promoted["previous"] == baseline["active"] + assert active_checkpoint(registry).read_bytes() == paths[1][0].read_bytes() + status = registry_status(registry) + assert status["status"] == "ready" + assert status["modelCount"] == 2 + assert status["automaticTraining"] is False + assert (registry / "checkpoints" / f"{baseline['active']}.pt").read_bytes() == previous_bytes + reverted = rollback(registry) + assert reverted["active"] == baseline["active"] + assert active_checkpoint(registry).read_bytes() == previous_bytes + assert len(list((registry / "checkpoints").glob("*.pt"))) == 2 + # A rejected promotion must not change active registry state. + bad = evaluation(load_checkpoint(paths[2][0])[1]["checkpointSha256"], .9) + bad["results"][0]["tracks"][0]["metrics"]["hardErrors"] = 1 + paths[2][1].write_text(json.dumps(bad)) + state_before = (registry / "registry.json").read_bytes() + with pytest.raises(ValueError, match="hard validation"): + register_checkpoint(registry, *paths[2]) + assert (registry / "registry.json").read_bytes() == state_before + + +def test_registry_rejects_tampered_active_artifact(tmp_path): + registry = tmp_path / "registry" + (registry / "checkpoints").mkdir(parents=True) + digest = "a" * 64 + (registry / "registry.json").write_text(json.dumps({"active": digest})) + (registry / "checkpoints" / f"{digest}.pt").write_bytes(b"wrong content") + with pytest.raises(ValueError, match="content hash"): + active_checkpoint(registry) + assert registry_status(registry)["status"] == "invalid" + + +def test_uninitialized_registry_has_honest_status(tmp_path): + assert registry_status(tmp_path / "missing") == { + "status": "empty", "active": None, "modelCount": 0, "automaticTraining": False, + } diff --git a/.codex/skills/beat-saber-mapping/tests/test_rl_training_correctness.py b/.codex/skills/beat-saber-mapping/tests/test_rl_training_correctness.py new file mode 100644 index 0000000..df361ee --- /dev/null +++ b/.codex/skills/beat-saber-mapping/tests/test_rl_training_correctness.py @@ -0,0 +1,167 @@ +"""Small deterministic RL regressions; no downloads or long training run.""" + +import json + +import numpy as np +import pytest + +torch = pytest.importorskip("torch") + +from rl.checkpoints import load_checkpoint, save_checkpoint +from rl.environment import BeatSaberEnv, decode_hand_action +from rl.evaluate import chart_metrics, evaluate_checkpoints +from rl.features import load_analysis_features +from rl.models import ActorCriticPolicy +from rl.policy_generator import RLMapGenerator +from rl.train_custom_tracks import extract_track_features, train_on_custom_library +from rl.train_ppo import RolloutBuffer, train_ppo +from test_rl_features import write_analysis +from kinematics import cross_hand_finding, vision_blocking_double + + +def test_terminal_last_slot_does_not_bootstrap_reset_episode_value(): + buffer = RolloutBuffer(1, 69) + buffer.ptr = 1 + buffer.rewards[0] = 3 + buffer.values[0] = 2 + buffer.dones[0] = 1 + buffer.compute_gae(last_value=100, gamma=.9) + assert buffer.returns[0].item() == pytest.approx(3) + assert torch.isfinite(buffer.advantages).all() + + +def test_gae_cuts_internal_episode_boundary_but_bootstraps_nonterminal_tail(): + buffer = RolloutBuffer(3, 69) + buffer.ptr = 3 + buffer.rewards[:] = torch.tensor([1, 2, 5]) + buffer.values[:] = torch.tensor([0, 0, 0]) + buffer.dones[:] = torch.tensor([0, 1, 0]) + buffer.compute_gae(last_value=10, gamma=.5, gae_lambda=1) + assert buffer.returns.tolist() == pytest.approx([2, 2, 10]) + + +def test_inference_requires_checkpoint_and_rejects_corrupt_weights(tmp_path): + with pytest.raises(ValueError, match="explicit trained checkpoint"): + RLMapGenerator() + with pytest.raises(FileNotFoundError): + RLMapGenerator(model_path=tmp_path / "missing.pt") + path = tmp_path / "broken.pt" + path.write_bytes(b"not a checkpoint") + with pytest.raises(ValueError, match="Invalid RL checkpoint"): + RLMapGenerator(model_path=path) + + +def test_checkpoint_hash_and_feature_provenance(tmp_path): + policy = ActorCriticPolicy(hidden_dim=32) + path = tmp_path / "policy.pt" + save_checkpoint(policy, path, {"seed": 4, "trainingSourceSha256": ["a" * 64]}) + loaded, metadata = load_checkpoint(path) + assert metadata["seed"] == 4 + assert len(metadata["checkpointSha256"]) == 64 + assert all(torch.equal(tensor, loaded.state_dict()[key]) for key, tensor in policy.state_dict().items()) + bad = policy.state_dict() + bad["encoder.0.weight"][0, 0] = float("nan") + torch.save(bad, path) + with pytest.raises(ValueError, match="non-finite"): + load_checkpoint(path) + + +def test_training_and_inference_load_identical_features(tmp_path): + path = write_analysis(tmp_path / "analysis") + audio, grid, bpm = extract_track_features(path) + bundle = load_analysis_features(path) + assert audio == bundle.audio_features + assert grid == bundle.beat_grid + assert bpm == bundle.bpm + assert audio["sections"][-1] == "chorus" + + +def test_short_ppo_run_stops_at_requested_steps_and_repeats_with_seed(): + states = [] + for _ in range(2): + torch.manual_seed(23) + policy = ActorCriticPolicy(hidden_dim=16) + env = BeatSaberEnv(beat_grid=[0, .25]) + metrics = train_ppo(policy, env, total_timesteps=3, rollout_steps=2, ppo_epochs=1, batch_size=2, seed=23) + assert metrics["timesteps"] == 3 + assert metrics["completed_episodes"] == 1 + assert np.isfinite(metrics["policy_loss"]) + states.append({key: value.clone() for key, value in policy.state_dict().items()}) + assert all(torch.equal(states[0][key], states[1][key]) for key in states[0]) + + +def test_environment_seed_does_not_mutate_numpy_global_rng(): + np.random.seed(321) + expected = np.random.random() + np.random.seed(321) + env = BeatSaberEnv(beat_grid=[0, .25]) + env.reset(seed=77) + assert np.random.random() == expected + assert not any(env.audio_features["stemAvailability"].values()) + + +def test_blue_mask_rejects_every_unsafe_simultaneous_pair(): + env = BeatSaberEnv(beat_grid=[0, .25]) + for red_action in range(1, env.action_dim): + red_pose = decode_hand_action(red_action) + red = {"b": 0, "c": 0, "x": red_pose[0], "y": red_pose[1], "d": red_pose[2]} + mask = env.blue_action_mask(red_action) + assert mask[0] + for blue_action in np.flatnonzero(mask[1:]) + 1: + x, y, direction = decode_hand_action(int(blue_action)) + blue = {"b": 0, "c": 1, "x": x, "y": y, "d": direction} + assert cross_hand_finding(red, blue) is None + assert not vision_blocking_double(red, blue) + + +def test_conditional_sampling_and_ppo_replay_have_identical_log_probabilities(): + torch.manual_seed(42) + env = BeatSaberEnv(beat_grid=[0, .25]) + policy = ActorCriticPolicy(hidden_dim=16) + obs = torch.as_tensor(env.reset()[0]).unsqueeze(0) + mask_red, mask_blue = env.action_masks() + conditional = lambda chosen: torch.as_tensor(env.blue_action_mask(int(chosen.item()), mask_blue)).unsqueeze(0) + action, sampled_log_prob, _, _ = policy.get_action_and_value( + obs, mask_red=torch.as_tensor(mask_red).unsqueeze(0), + mask_blue=torch.as_tensor(mask_blue).unsqueeze(0), blue_mask_fn=conditional, + ) + effective_blue = conditional(action[0]) + assert effective_blue[0, int(action[1].item())] + _, replay_log_prob, _, _ = policy.get_action_and_value( + obs, action=action, mask_red=torch.as_tensor(mask_red).unsqueeze(0), mask_blue=effective_blue, + ) + assert sampled_log_prob.item() == pytest.approx(replay_log_prob.item()) + + +def test_benchmark_uses_held_out_hashes_and_reports_real_validation(tmp_path): + write_analysis(tmp_path / "train", source="a" * 64) + write_analysis(tmp_path / "held-out", source="b" * 64) + manifest = tmp_path / "library.json" + manifest.write_text(json.dumps({"schemaVersion": 1, "tracks": [ + {"id": "train", "analysisDir": "train", "split": "train"}, + {"id": "held-out", "analysisDir": "held-out", "split": "validation"}, + ]})) + policy = ActorCriticPolicy(hidden_dim=16) + for parameter in policy.parameters(): + parameter.data.zero_() # Deterministic idle policy, only for the fixture. + path = tmp_path / "policy.pt" + save_checkpoint(policy, path, {"trainingSourceSha256": ["a" * 64]}) + result = evaluate_checkpoints(manifest, [path], seed=3) + assert result["heldOutVerifiedAgainstRecordedTraining"] + assert result["results"][0]["tracks"][0]["id"] == "held-out" + assert result["results"][0]["tracks"][0]["metrics"]["notes"] == 0 + assert result["results"][0]["tracks"][0]["metrics"]["headsetPlaytest"] == "not_performed" + save_checkpoint(policy, path, {"trainingSourceSha256": ["b" * 64]}) + with pytest.raises(ValueError, match="used to train"): + evaluate_checkpoints(manifest, [path]) + + +def test_structural_benchmark_detects_colliding_notes(tmp_path): + bundle = load_analysis_features(write_analysis(tmp_path / "analysis")) + chart = {"version": "3.3.0", "colorNotes": [ + {"b": 1, "c": 0, "x": 1, "y": 0, "d": 1}, + {"b": 1, "c": 1, "x": 1, "y": 0, "d": 1}, + ]} + result = chart_metrics(chart, bundle, "Expert") + assert result["hardErrors"] > 0 + assert result["nps"] == pytest.approx(.8) diff --git a/.github/workflows/skill-drift.yml b/.github/workflows/skill-drift.yml index 899e9d2..e6935e3 100644 --- a/.github/workflows/skill-drift.yml +++ b/.github/workflows/skill-drift.yml @@ -21,7 +21,7 @@ jobs: - uses: actions/setup-python@v5 with: python-version: "3.12" - - run: python -m pip install -e ".[dev]" + - run: python -m pip install -e ".[dev,studio]" - run: python -m playwright install --with-deps chromium - run: python -m pytest tests -q -m "not network and not hardware and not codex" diff --git a/.gitignore b/.gitignore index 93dcf1f..71b4ec3 100644 --- a/.gitignore +++ b/.gitignore @@ -31,3 +31,6 @@ BeatSaberVersion.txt .DS_Store Thumbs.db site/ +!web/assets/demo/song.ogg +data/learning.json +data/model_registry/ diff --git a/README.md b/README.md index f52ad57..cc2f69a 100644 --- a/README.md +++ b/README.md @@ -17,6 +17,15 @@ pip install -e ".[dev]" python skills\beat-saber-mapping\scripts\bootstrap.py --tier core ``` +For a guided Windows setup and a read-only dependency check, use the included launcher: + +```powershell +.\Start-BeatForge.ps1 -Setup -Check +.\Start-BeatForge.ps1 +``` + +The launcher never downloads model weights during its doctor pass. It reports missing optional trackers and the local official corpus separately from the dependencies required to open Studio. + Optional audio models (Beat This, BeatNet+, All-In-One, Demucs) need a working local PyTorch install. If `torch_python.dll` fails to load from the bootstrap `--target` cache, install Torch into the project venv instead of weakening @@ -54,6 +63,20 @@ The optional Codex review key lives in Windows Credential Manager as `BeatForge:codex`. The studio writes it from the Codex setup dialog; it never prints it back. +## Feedback and self-improvement + +Studio records only explicit local feedback against the exact chart hash, audio hash, difficulty, tester, and optional beat range. Presets, A/B preferences, and accepted revisions are exportable from `/api/learning/export` without copying song audio. Suggestions are bounded adjustments derived from repeated feedback; they are shown for approval and never silently retrain or rewrite a map. + +The RL path requires an explicit checkpoint and uses the same sample-aligned analysis features for training, evaluation, and generation. Held-out reports and content hashes are required before a checkpoint can enter `data/model_registry`; promotion is blocked by hard validation failures or quality regressions, and `rollback` restores the prior immutable version. Training reward and structural QA are reported separately from human preferences and headset evidence. + +Run the regression suites with isolated temporary files: + +```powershell +.venv\Scripts\python.exe -m pytest tests -q -m "not corpus and not network and not hardware and not codex" +$env:PYTHONPATH = "skills/beat-saber-mapping/scripts" +.venv\Scripts\python.exe -m pytest skills/beat-saber-mapping/tests -q -m "not corpus" +``` + ## Failure recovery | Studio state | What happened | What to do | diff --git a/Start-BeatForge.ps1 b/Start-BeatForge.ps1 new file mode 100644 index 0000000..6b04fbc --- /dev/null +++ b/Start-BeatForge.ps1 @@ -0,0 +1,30 @@ +param( + [switch]$Setup, + [switch]$Check, + [ValidateRange(1024, 65535)][int]$Port = 8001 +) +$ErrorActionPreference = 'Stop' +$studioRoot = $PSScriptRoot +$studioPython = Join-Path $studioRoot '.venv\Scripts\python.exe' +Push-Location -LiteralPath $studioRoot +try { + if ($Setup) { + if (-not (Test-Path -LiteralPath $studioPython)) { + python -m venv .venv + if ($LASTEXITCODE -ne 0) { throw 'Install Python 3.11 or newer, then retry -Setup.' } + } + & $studioPython -m pip install -e '.[dev,studio]' + if ($LASTEXITCODE -ne 0) { throw 'Dependency installation failed. Check the pip output above.' } + } + if (-not (Test-Path -LiteralPath $studioPython)) { + throw 'The project environment is missing. Run .\Start-BeatForge.ps1 -Setup first.' + } + & $studioPython tools\doctor.py + if ($LASTEXITCODE -ne 0) { throw 'Fix the required dependency checks above before starting Studio.' } + if ($Check) { return } + Write-Host "Open http://127.0.0.1:$Port/ to use BeatForge Studio. Press Ctrl+C to stop." + & $studioPython -m uvicorn beatforge.api:app --host 127.0.0.1 --port $Port + if ($LASTEXITCODE -ne 0) { throw 'Studio stopped with an error. Check the server output above.' } +} finally { + Pop-Location +} diff --git a/docs/IMPROVEMENT_PLAN.md b/docs/IMPROVEMENT_PLAN.md new file mode 100644 index 0000000..3cfa2ec --- /dev/null +++ b/docs/IMPROVEMENT_PLAN.md @@ -0,0 +1,77 @@ +# BeatForge improvement plan + +This implementation follows the repository review at `5c0cfb9`. The aim is a studio where creative direction changes the generated chart, the result can be heard and inspected, and a selected passage can be revised without losing the rest of the work. + +## 1. Reliable generation and recovery + +- Repair the browser metadata field contract and both supported timing-anchor formats. +- Require readable, passing QA before a pack is eligible for installation. Preserve a clearly labeled unverified timing workflow without treating structural errors as acceptable. +- Make later failed playtests supersede earlier matching clears. +- Preserve job history, identify interrupted jobs after restart, and offer retry and cancellation. +- Verify these behaviors with real API requests and isolated installation doubles. + +## 2. Creative direction and musical structure + +- Introduce a versioned mapping plan shared by the browser, API, CLI, generator, and provenance. +- Support intensity, density, instrument emphasis, style, verse/chorus contrast, and optional bombs/walls. +- Interpret a documented set of brief phrases deterministically, display the applied settings, and never pretend unrestricted prose understanding. +- Improve phrase consistency and rank safe choreography candidates using musical and corpus evidence. +- Verify that the same seed and plan reproduce output, that different plans change intended behavior, and that hard constraints remain intact. + +## 3. Inspect and revise maps + +- Add an audio-synchronized perspective chart preview, waveform, difficulty selection, seeking, playback speed, section markers, and QA markers. +- Load actual generated map objects through a confined preview API. +- Regenerate a selected beat range into a separate revision, preserve objects outside that range, revalidate seams and the complete result, and retain the original job. +- Provide a real synthetic sample with audio and chart data for local and static previews. Clearly distinguish a sample from the full generation pipeline. +- Verify the browser controls, audio synchronization, actual note data, and revision preservation. + +## 4. Correct and evaluate learning + +- Share beat/sample-aligned feature extraction between training and inference. Use actual section labels and available stem energies, record missing features, and remove invented stems and the fixed 120 BPM clock. +- Require valid inference checkpoints and record their identity. +- Correct terminal bootstrapping in PPO and make training reproducible. +- Add held-out evaluation that reports map quality separately from training loss and reward. +- Verify feature timing, section alignment, checkpoint errors, terminal boundaries, and benchmark output with deterministic fixtures. + +## 5. Setup, verification, and delivery + +- Provide a local launcher and actionable dependency checks. +- Keep the canonical and portable skill copies synchronized. +- Run relevant application and mapping suites, a local Studio browser pass, and generated-pack validation. +- Record test results and limitations. A visual preview or automated validator cannot establish headset comfort or a human clear. +- Deliver the updated repository and instructions for starting it locally. Remote publishing is a separate action from local implementation. + +## 6. A measured self-improvement loop + +User expansion: make BeatForge self-improving and add features that improve the experience. This extends the active implementation goal. + +- Save explicit feedback against the exact chart hash, difficulty, and beat range: timing, flow, readability, repetition, intensity, and freeform headset notes. +- Record A/B preferences between two maps of the same audio and difficulty. Keep an unmodified baseline and retain failed experiments for diagnosis. +- Treat accepted section revisions as examples of user preference. Export a local dataset with versioned provenance and without copying song audio. +- Add a model registry with benchmark results, active checkpoint, prior checkpoint, and rollback. Promotion requires matching held-out benchmark identity, no hard failures, and improvement on the selected quality metric without defined regressions. +- Keep training results, structural validation, and human preference scores separate. The app must never invent ratings, human clears, or claim that a promoted checkpoint has been played. +- Provide a visible improvement dashboard explaining evidence collected, outstanding evaluations, and the next useful action. Training and promotion remain explicit operations with visible results. + +## 7. Faster everyday use + +- Save and reuse personal creative presets, including instrument focus, density, style, and accessibility choices. +- Compare two revisions in the preview at the same audio position and difficulty, then save a preference. +- Show actionable QA findings with beat navigation and retain section feedback alongside revisions. +- Restore the last selected local job after refresh, show interrupted work clearly, and allow recovery without re-uploading audio. +- Expose useful preview, feedback, and comparison operations through typed WebMCP tools with the same validation as the visible controls. + +## Progress + +- [x] Review current repository and create implementation branch. +- [x] Reliable generation and recovery (API lifecycle coverage and durable retry/cancel state). +- [x] Creative controls and musical structure (versioned plans, deterministic brief interpretation, candidate ranking). +- [x] Real map preview and section revisions (audio clock, waveform, QA navigation, isolated range regeneration). +- [x] Learning correctness and evaluation (sample-aligned features, masked PPO, held-out evaluation, checkpoint registry). +- [x] Setup, integration, regression checks, and delivery audit (launcher, doctor, portable lock, browser and API suites). +- [x] Feedback dataset, evaluation-gated model registry, and improvement dashboard. +- [x] Personal presets and comparison workflow. + +The software-side gates are complete. A real headset session remains a human release activity: sight-read each selected difficulty at full and slow speed, inspect comfort and reach, and record that evidence through the Studio before treating a map as cleared. + +The goal remains active while any required implementation or verification is incomplete. diff --git a/pyproject.toml b/pyproject.toml index 0df64d1..f62455a 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -26,6 +26,8 @@ dependencies = [ [project.optional-dependencies] dev = ["pytest==8.4.2", "playwright==1.55.0"] +studio = ["ortools==9.15.6755"] +rl = ["torch>=2.6,<3"] [project.scripts] beatforge = "beatforge.cli:main" diff --git a/skill-lock.json b/skill-lock.json index 87d0caf..782d810 100644 --- a/skill-lock.json +++ b/skill-lock.json @@ -25,28 +25,41 @@ "scripts/artwork.py": "14125e7137951a3e43303e39c70e802d630bc34b14f8134222075dfdb6a5e941", "scripts/beatforge_core.py": "d42516074d1742af6af78fa13e45ef4b322a483a93b13b5ee3eb60402979acfd", "scripts/bootstrap.py": "8ba79ea3b0ad0ab96a8f6fa562634d6ac6eabd7b43983d19d74cae7ab8ba9dd8", - "scripts/choreography.py": "dfb9d8e9d41e570c86a9e9820dd38d04031d047a8bf51a0657d67dd1a4e99300", + "scripts/choreography.py": "74cb70841ef0b0c8c31aaf7a7833591968a74890350636ee4585cd2dda62a204", "scripts/demucs_stems.py": "3e08440751ae131ad4b9c9f41639b29e98e9e7cc750b54a6c814650e3bff0749", - "scripts/generate_map.py": "bf99db96ea6e0a92c18c745cb426667fb06e39e9bb6cc6a00d738c39518145b8", + "scripts/generate_map.py": "a9fc9052f0b2dc70df01f95fd54f4350cada2e6bf7c2f1dd7162a1fdf7b849ae", "scripts/inspect_map.py": "72675ed6d1a1eda3dc8459c9a86014f6cac1d44fed006385237835c846d88567", "scripts/kinematics.py": "6bfdae6359f335fb143612bfece8509b10c425de852780ffa39077e36a8834ec", + "scripts/mapping_plan.py": "ce42bb69f509d7038244f7b1edae7f20af8f5026d18134fa0f915aa3dd73788d", "scripts/official_corpus.py": "d95c07dd6c613bb483922aa61ce97833bc8c8bf71985df7edbdf03014b847b40", - "scripts/rl/__init__.py": "4794d851da5b10426caa8fc6295788414af9dd5456a19b9cb04c17021c2e172a", + "scripts/rl/README.md": "fb453934ad22b82a1e0a6f2ec2ed3f7de528cbffbb8d7dac9604f60a5d1cb954", + "scripts/rl/__init__.py": "4ca12ccbe2ed1f8f3cc33c9e05a0ba12c80a8680479899b81a2f3eb4385add3d", + "scripts/rl/checkpoints.py": "396d04dbec6cbda7f4e064559bd0cf9cb1d4426994b91dfd387ce2b1d5897c18", "scripts/rl/dataset.py": "674b122175ef8cea9422c22f3f356463c29540ecb7d2644d746f86d2324a6fbc", "scripts/rl/discriminator.py": "ef5fdb1e5080b6620cf7f5a6311024fb7216130d7ebc89b8c029248327b69862", - 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0.35 * intensity)) + density = float(section.get("density", 1.0)) + adaptive_gap = max(gap, adaptive_gap / max(0.35, density)) if beat - last_beat + 1e-9 < adaptive_gap: continue if any( @@ -194,13 +197,16 @@ def select_intents(analysis: dict[str, Any], sections: dict[str, Any], difficult continue strength = float(event.get("strength", 0.0)) energies = event.get("stemEnergy") if isinstance(event.get("stemEnergy"), dict) else {} + dominant = str(section.get("dominantInstrument", "auto")) + if dominant != "auto" and dominant in energies: + strength *= 0.7 + 1.1 * max(0.0, min(1.0, float(energies[dominant]))) layer = str(event.get("layer") or "") instrument = float(energies.get("drums") or 0.0) + float(energies.get("bass") or 0.0) + float(energies.get("guitar") or 0.0) vocal = float(energies.get("vocals") or 0.0) layered = bool(energies) and instrument >= vocal * 1.15 and layer in {"drums", "bass", "guitar", "piano", "other"} if difficulty in {"Expert", "ExpertPlus"} and layered: strength *= 1.35 - threshold = base_threshold + threshold = base_threshold / max(0.35, density) if is_peak: threshold *= 0.88 if difficulty in {"Expert", "ExpertPlus"} else 0.94 elif intensity < 0.4: @@ -256,6 +262,15 @@ def select_intents(analysis: dict[str, Any], sections: dict[str, Any], difficult "layer": layer or "mix", "stemEnergy": energies, } + if "motifId" in section: + relative = beat - float(section.get("startBeat", 0.0)) + motif_offset = int(section.get("motifOffset", 0)) + payload.update({ + "sectionId": section["id"], "motifId": section["motifId"], + "patternBeat": relative + motif_offset * 4.0, + "patternIndex": int(round(relative * 2.0)) + motif_offset, + "style": section.get("style", "balanced"), + }) selected.append(payload) last_beat = beat return selected @@ -433,6 +448,7 @@ def _candidate_notes( index: int, intensity: float, beat: float, + preference_beat: float | None = None, ) -> list[dict[str, Any]]: """Enumerate combo-first arrow poses. Dots are a rare reset, not the default cut.""" @@ -440,8 +456,9 @@ def _candidate_notes( home = (0, 1) else: home = (3, 2) - preferred = swing_family(color, parity, beat) - lead = cut_for(color, parity, index, beat) + phase = beat if preference_beat is None else preference_beat + preferred = swing_family(color, parity, phase) + lead = cut_for(color, parity, index, phase) notes: list[dict[str, Any]] = [] seen: set[tuple[int, int, int]] = set() @@ -627,9 +644,14 @@ def _pose_domain( intensity = float(intent.get("intensity", 0.5)) kind = intent["kind"] duration = float(intent.get("duration") or 0.0) - heads = _candidate_notes(color, parity, index, intensity, beat) + index = int(intent.get("patternIndex", index)) + int(intent.get("candidateVariant", 0)) + phase = float(intent.get("patternBeat", beat)) + heads = _candidate_notes(color, parity, index, intensity, beat, preference_beat=phase) + if intent.get("style") in {"flow", "chill"}: + home_x = 0.5 if color == 0 else 2.5 + heads.sort(key=lambda head: abs(float(head["x"]) - home_x) + (0.5 if int(head["d"]) in {2, 3, 8} else 0.0)) if str(intent.get("layer") or "") == "combo-stack": - lead = cut_for(color, parity, index, beat) + lead = cut_for(color, parity, index, phase) if color == 0: pair_cells = ((1, 1), (0, 1), (1, 0), (1, 2)) else: @@ -667,7 +689,9 @@ def _joint_combined_domain( beat = float(intent["beat"]) stack = str(intent.get("layer") or "") == "combo-stack" - lead_parity = int(beat // 4.0) % 2 if stack else (index + int(beat // 2.0)) % 2 + pattern_beat = float(intent.get("patternBeat", beat)) + pattern_index = int(intent.get("patternIndex", index)) + int(intent.get("candidateVariant", 0)) + lead_parity = int(pattern_beat // 4.0) % 2 if stack else (pattern_index + int(pattern_beat // 2.0)) % 2 buckets = { (color_id, parity): _pose_domain(intent, color_id, parity, index, bpm, recovery_beats)[:6] for color_id in (0, 1) @@ -2500,12 +2524,13 @@ def score_candidate(database_path: Path | None, features: dict[str, Any]) -> dic } -def generate_all( +def _generate_single_pass( analysis: dict[str, Any], sections: dict[str, Any], seed: int, corpus_database: Path | None = None, difficulties: Sequence[str] | None = None, + candidate_variant: int = 0, ) -> tuple[dict[str, dict[str, Any]], dict[str, Any]]: generated: dict[str, dict[str, Any]] = {} report: dict[str, Any] = {"solver": {}, "difficulties": {}, "officialReferences": {}} @@ -2513,6 +2538,8 @@ def generate_all( for difficulty in chosen: emit_progress("choreography", f"selecting {difficulty} intents from onsets and sections") intents = select_intents(analysis, sections, difficulty) + if candidate_variant: + intents = [{**intent, "candidateVariant": candidate_variant} for intent in intents] joint = None try: emit_progress("choreography", f"solving joint CP-SAT for {difficulty} ({len(intents)} events)") @@ -2559,3 +2586,97 @@ def generate_all( ) report["difficulties"][difficulty]["corpusRank"] = score_candidate(corpus_database, candidate_features) return generated, report + + +def rank_choreography_candidate( + beatmap: dict[str, Any], difficulty: str, bpm: float, + corpus_rank: dict[str, Any], reference: dict[str, Any], + expected_count: int, +) -> dict[str, Any]: + """Score complete candidates only after running the unchanged structural gates.""" + from beatforge_core import ValidationReport + from validate_map import validate_v3 + + validation = ValidationReport() + validate_v3(beatmap, f"{difficulty}Standard.dat", validation, bpm=bpm, difficulty=difficulty) + notes = beatmap.get("colorNotes", []) + errors = [issue.code for issue in validation.errors] + if expected_count and not notes: + errors.append("NO_PLAYABLE_NOTES") + events, _ = normalize_beatmap(beatmap) + features = map_features(events, bpm) + profile = reference.get("profile", {}) + comparable = [name for name in ("p95Reach", "centerVisionRate", "medianSameHandGap") if name in profile] + profile_distance = sum(abs(float(features.get(name, 0.0)) - float(profile[name])) / max(0.25, abs(float(profile[name]))) for name in comparable) / max(1, len(comparable)) + reference_score = 1.0 / (1.0 + profile_distance) if comparable else 0.5 + corpus_score = float(corpus_rank.get("score", 50.0)) / 100.0 + coverage = min(1.0, len(notes) / max(1, expected_count)) + signatures = [(int(note["x"]), int(note["y"]), int(note["d"])) for note in notes] + repeats = sum(left == right for left, right in zip(signatures, signatures[1:])) + variety = 1.0 - repeats / max(1, len(signatures) - 1) + score = 100.0 * (0.4 * corpus_score + 0.25 * reference_score + 0.25 * coverage + 0.1 * variety) + return { + "score": round(score, 6), "hardErrors": sorted(set(errors)), + "eligible": not errors, "corpusScore": round(corpus_score * 100.0, 6), + "referenceScore": round(reference_score * 100.0, 6), + "eventCoverage": round(coverage, 6), "variety": round(variety, 6), + "features": features, + } + + +def select_ranked_candidate(candidates: list[dict[str, Any]]) -> dict[str, Any]: + eligible = [candidate for candidate in candidates if candidate.get("eligible")] + if not eligible: + codes = sorted({code for candidate in candidates for code in candidate.get("hardErrors", [])}) + raise ValueError("No candidate passed the unchanged safety gates: " + ", ".join(codes)) + return max(eligible, key=lambda candidate: (candidate["score"], -candidate["index"])) + + +def generate_all( + analysis: dict[str, Any], sections: dict[str, Any], seed: int, + corpus_database: Path | None = None, + difficulties: Sequence[str] | None = None, + mapping_plan: dict[str, Any] | None = None, +) -> tuple[dict[str, dict[str, Any]], dict[str, Any]]: + if mapping_plan is None: + return _generate_single_pass(analysis, sections, seed, corpus_database, difficulties) + controls = normalize_mapping_plan(mapping_plan) + planned = build_section_plan(analysis, sections, controls) + chosen = tuple(name for name in DIFFICULTIES if name in set(difficulties or DIFFICULTIES)) or DIFFICULTIES + generated: dict[str, dict[str, Any]] = {} + report: dict[str, Any] = {"solver": {}, "difficulties": {}, "officialReferences": {}, "candidateRanking": {}, "mappingPlan": planned} + for difficulty in chosen: + # Retrieve before generation so every candidate is compared with the same reference family. + reference = retrieve_official_references(corpus_database, difficulty, float(analysis["bpm"])) + intents = select_intents(analysis, planned, difficulty) + candidates = [] + payloads: dict[int, tuple[dict[str, Any], dict[str, Any]]] = {} + for index in range(controls["candidateCount"]): + emit_progress("choreography", f"{difficulty}: generating candidate {index + 1}/{controls['candidateCount']}") + try: + maps, trace = _generate_single_pass(analysis, planned, seed + index * 7919, corpus_database, (difficulty,), candidate_variant=index) + beatmap = maps[difficulty] + if controls["noBombs"]: + beatmap["bombNotes"] = [] + if controls["noWalls"]: + beatmap["obstacles"] = [] + ranked_map = beatmap + if len(analysis.get("beatGrid", [])) >= 2: + from timing_export import project_map_timing + ranked_map = project_map_timing(beatmap, analysis) + ranked_events, _ = normalize_beatmap(ranked_map) + corpus_rank = score_candidate(corpus_database, map_features(ranked_events, float(analysis["bpm"]))) + rank = rank_choreography_candidate(ranked_map, difficulty, float(analysis["bpm"]), corpus_rank, reference, len(intents)) + trace["difficulties"][difficulty]["corpusRank"] = corpus_rank + candidates.append({"index": index, **rank}) + payloads[index] = (beatmap, trace) + except (RuntimeError, ValueError) as error: + candidates.append({"index": index, "eligible": False, "score": 0.0, "hardErrors": [str(error)]}) + winner = select_ranked_candidate(candidates) + beatmap, trace = payloads[winner["index"]] + generated[difficulty] = beatmap + report["solver"][difficulty] = trace["solver"][difficulty] + report["difficulties"][difficulty] = trace["difficulties"][difficulty] + report["officialReferences"][difficulty] = reference + report["candidateRanking"][difficulty] = {"selectedIndex": winner["index"], "candidates": candidates} + return generated, report diff --git a/skills/beat-saber-mapping/scripts/generate_map.py b/skills/beat-saber-mapping/scripts/generate_map.py index 81d79be..f189144 100644 --- a/skills/beat-saber-mapping/scripts/generate_map.py +++ b/skills/beat-saber-mapping/scripts/generate_map.py @@ -17,6 +17,8 @@ from beatforge_core import DIFFICULTIES, TOOL_VERSION, default_cache_dir, difficulty_rank, emit_progress, encode_ogg, find_ffmpeg, load_json, parse_difficulties, sha256_file, write_json from choreography import CONFIGS, generate_all +from mapping_plan import build_section_plan, normalize_mapping_plan +from timing_export import project_map_timing, project_plan_timing from artwork import derive_palette, extract_embedded_artwork, lookup_itunes_cover, lookup_release_cover, palette_from_rgb, write_palette_cover from official_corpus import corpus_is_fresh, detect_game_root, sync as sync_official_corpus @@ -373,6 +375,7 @@ def main() -> int: parser.add_argument("--profile", default="official-premium", choices=["official-premium", "official-rl"]) parser.add_argument("--engine", default="cp-sat", choices=["cp-sat", "rl", "hybrid"], help="Choreography engine to use (cp-sat, rl, hybrid)") parser.add_argument("--rl-model", type=Path, help="Path to trained PyTorch RL policy checkpoint") + parser.add_argument("--mapping-plan", type=Path, help="Version 1 structured creative controls JSON") parser.add_argument("--environment", default=None, help="Beat Saber environment name (e.g. DaftPunkEnvironment, DefaultEnvironment)") parser.add_argument("--full-spread", action="store_true", default=True) parser.add_argument( @@ -393,6 +396,13 @@ def main() -> int: parser.add_argument("--game-root", type=Path, help="Beat Saber install root; auto-detected on common Oculus/Steam paths") parser.add_argument("--seed", type=int) args = parser.parse_args() + try: + creative_controls = normalize_mapping_plan(load_json(args.mapping_plan)) if args.mapping_plan else None + if (args.engine == "rl" or args.profile == "official-rl") and (args.rl_model is None or not args.rl_model.is_file()): + raise ValueError("The RL engine requires an existing trained checkpoint supplied with --rl-model") + except (ValueError, OSError) as error: + print(f"ERROR: {error}", file=sys.stderr) + return 2 if not args.audio.is_file(): print(f"ERROR: audio file does not exist: {args.audio}", file=sys.stderr) return 2 @@ -423,6 +433,7 @@ def main() -> int: emit_progress("timing", "reusing existing analysis.json") analysis = load_json(analysis_file) sections = load_json(sections_file) + creative_plan = build_section_plan(analysis, sections, creative_controls) if creative_controls is not None else None unconfirmed = bool(args.continue_unconfirmed) if analysis.get("status") != "timing_verified": if not unconfirmed: @@ -496,54 +507,33 @@ def main() -> int: emit_progress("choreography", f"solving {', '.join(chosen)}") if args.engine == "rl" or args.profile == "official-rl": from rl.policy_generator import RLMapGenerator + from rl.features import load_analysis_features rl_gen = RLMapGenerator(model_path=args.rl_model) - raw_bg = load_json(analysis_dir / "beat_grid.json") if (analysis_dir / "beat_grid.json").exists() else None - if isinstance(raw_bg, dict) and "tempoRegions" in raw_bg: - max_beat = max((float(r.get("endBeat", 0.0)) for r in raw_bg.get("tempoRegions", [])), default=0.0) - beat_grid = [round(i * 0.25, 4) for i in range(max(16, int(max_beat * 4)))] - elif isinstance(raw_bg, list): - beat_grid = [float(b) for b in raw_bg] - else: - duration_s = float(analysis.get("durationSeconds", analysis.get("duration", 240.0))) - bpm_val = float(analysis.get("bpm", 125.0)) - beat_grid = [round(i * 0.25, 4) for i in range(max(16, int(duration_s * bpm_val / 60.0 * 4)))] - - grid_len = len(beat_grid) - try: - import numpy as np - from beatforge_core import frame_features, load_audio - audio_buf = load_audio(args.audio) - feat = frame_features(audio_buf.samples) - flux = feat["flux"] - onsets = np.interp(np.linspace(0, len(flux), grid_len), np.arange(len(flux)), flux) - onsets = ((onsets - np.min(onsets)) / (np.max(onsets) - np.min(onsets) + 1e-6)).tolist() - except Exception: - onsets = [0.5] * grid_len - - audio_features = { - "onsets": onsets, - "flux": onsets, - "stems": { - "drums": onsets, - "bass": onsets, - "vocals": [o * 0.7 for o in onsets], - "guitar": [o * 0.5 for o in onsets], - "piano": [o * 0.5 for o in onsets], - "other": [o * 0.4 for o in onsets], - }, - "sections": [s.get("type", "verse") for s in sections] if isinstance(sections, list) else ["verse"] * grid_len, - } + bundle = load_analysis_features(analysis_dir, require_verified=not unconfirmed) maps = {} for diff in chosen: - maps[diff] = rl_gen.generate_difficulty(audio_features, beat_grid, float(analysis["bpm"]), difficulty=diff) + maps[diff] = rl_gen.generate_difficulty(bundle.audio_features, bundle.beat_grid, bundle.bpm, difficulty=diff, seed=seed) + if creative_controls and creative_controls["noBombs"]: + maps[diff]["bombNotes"] = [] + if creative_controls and creative_controls["noWalls"]: + maps[diff]["obstacles"] = [] choreography_report = { "poseSolver": "rl-policy-v1", "solver": "rl-policy-v1", "difficulties": chosen, + "checkpoint": rl_gen.checkpoint_metadata, + "features": bundle.provenance, + "mappingPlan": creative_plan, + "creativeControlSupport": "RL applies noBombs and noWalls only; phrase, density, and style controls require the premium engine", } else: - maps, choreography_report = generate_all(analysis, sections, seed, args.corpus_database, difficulties=chosen) + maps, choreography_report = generate_all(analysis, sections, seed, args.corpus_database, difficulties=chosen, mapping_plan=creative_controls) + maps = {difficulty: project_map_timing(payload, analysis) for difficulty, payload in maps.items()} + if creative_plan is not None: + creative_plan = project_plan_timing(creative_plan, analysis) + choreography_report["mappingPlan"] = creative_plan + write_json(package_analysis_dir / "mapping_plan.json", creative_plan) for difficulty, payload in maps.items(): write_json(args.out / f"{difficulty}Standard.dat", payload) emit_progress("audio", "encoding song.ogg for the Beat Saber pack") @@ -590,6 +580,7 @@ def main() -> int: }, }, "choreography": choreography_report, + "mappingPlan": creative_plan, "officialCorpus": {"database": str(args.corpus_database.resolve()), **corpus_report}, "documentation": { "manifest": "_beatforge/documentation_manifest.json", diff --git a/skills/beat-saber-mapping/scripts/mapping_plan.py b/skills/beat-saber-mapping/scripts/mapping_plan.py new file mode 100644 index 0000000..7e4a0bb --- /dev/null +++ b/skills/beat-saber-mapping/scripts/mapping_plan.py @@ -0,0 +1,133 @@ +"""Versioned, deterministic creative controls shared by Studio and the portable mapper.""" + +from __future__ import annotations + +import math +import re +from typing import Any + +SCHEMA_VERSION = 1 +INSTRUMENTS = ("auto", "drums", "bass", "vocals", "guitar", "piano", "other") +STYLES = ("balanced", "flow", "tech", "chill") +SCALE_FIELDS = ("intensity", "density", "verseDensity", "chorusDensity") +DEFAULTS = { + "schemaVersion": SCHEMA_VERSION, "brief": "", "intensity": 1.0, + "density": 1.0, "verseDensity": 1.0, "chorusDensity": 1.0, + "dominantInstrument": "auto", "style": "balanced", "noBombs": False, + "noWalls": False, "candidateCount": 2, "sectionOverrides": [], +} + + +def _scale(value: Any, field: str) -> float: + if isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value) or not 0.5 <= value <= 1.5: + raise ValueError(f"{field} must be a number from 0.5 to 1.5") + return float(value) + + +def normalize_mapping_plan(value: dict[str, Any] | None = None) -> dict[str, Any]: + """Resolve supported brief phrases, with explicit controls taking precedence.""" + if value is None: + value = {} + if not isinstance(value, dict): + raise ValueError("mapping plan must be an object") + unknown = set(value) - set(DEFAULTS) - {"interpretation"} + if unknown: + raise ValueError("unknown mapping plan controls: " + ", ".join(sorted(unknown))) + if type(value.get("schemaVersion", 1)) is not int or value.get("schemaVersion", 1) != 1: + raise ValueError("mapping plan schemaVersion must be 1") + brief = value.get("brief", "") + if not isinstance(brief, str) or len(brief) > 2000: + raise ValueError("brief must be text of at most 2000 characters") + text = brief.casefold() + inferred: dict[str, Any] = {} + recognized: list[str] = [] + phrases = [ + (r"\b(?:no|without) bombs\b", "noBombs", True, "No bombs"), + (r"\b(?:no|without) walls\b", "noWalls", True, "No walls"), + (r"\b(?:sparse|lighter|easier) verses?\b", "verseDensity", 0.75, "Lighter verses"), + (r"\b(?:dense|denser|bigger|stronger) chorus(?:es)?\b", "chorusDensity", 1.25, "Denser choruses"), + (r"\b(?:less|fewer) notes\b", "density", 0.75, "Fewer notes"), + (r"\bmore notes\b", "density", 1.25, "More notes"), + (r"\b(?:relaxed|chill)\b", "style", "chill", "Chill style"), + (r"\b(?:flow|flowing|smooth)\b", "style", "flow", "Flow style"), + (r"\b(?:tech|technical)\b", "style", "tech", "Tech style"), + ] + for pattern, field, setting, description in phrases: + if re.search(pattern, text): + inferred[field] = setting + if field not in value: + recognized.append(description) + for instrument in INSTRUMENTS[1:]: + if re.search(r"\b(?:follow|focus on|emphasize) (?:the )?" + instrument + r"\b", text): + inferred["dominantInstrument"] = instrument + if "dominantInstrument" not in value: + recognized.append("Follow " + instrument) + result = {**DEFAULTS, **inferred, **value, "brief": brief} + for field in SCALE_FIELDS: + result[field] = _scale(result[field], field) + if result["dominantInstrument"] not in INSTRUMENTS: + raise ValueError("unknown dominantInstrument") + if result["style"] not in STYLES: + raise ValueError("unknown mapping style") + for field in ("noBombs", "noWalls"): + if not isinstance(result[field], bool): + raise ValueError(f"{field} must be a boolean") + count = result["candidateCount"] + if isinstance(count, bool) or not isinstance(count, int) or not 1 <= count <= 3: + raise ValueError("candidateCount must be an integer from 1 to 3") + overrides = result["sectionOverrides"] + if not isinstance(overrides, list) or len(overrides) > 200: + raise ValueError("sectionOverrides must be a list with at most 200 entries") + checked = [] + seen = set() + for override in overrides: + if not isinstance(override, dict) or set(override) - {"id", "density", "intensity", "dominantInstrument"}: + raise ValueError("invalid section override") + section_id = override.get("id") + if not isinstance(section_id, str) or not section_id or len(section_id) > 100 or section_id in seen: + raise ValueError("section overrides need unique nonempty ids") + seen.add(section_id) + item = dict(override) + for field in ("density", "intensity"): + if field in item: + item[field] = _scale(item[field], "section " + field) + if "dominantInstrument" in item and item["dominantInstrument"] not in INSTRUMENTS: + raise ValueError("unknown section dominantInstrument") + checked.append(item) + result["sectionOverrides"] = checked + interpretation = value.get("interpretation", []) + if not isinstance(interpretation, list) or not all(isinstance(item, str) for item in interpretation): + raise ValueError("interpretation must be a list of phrases") + result["interpretation"] = recognized or list(interpretation) + return result + + +def build_section_plan(analysis: dict[str, Any], sections: dict[str, Any], value: dict[str, Any]) -> dict[str, Any]: + plan = normalize_mapping_plan(value) + source = sections.get("sections") or [{"label": "body", "startBeat": 0.0, "endBeat": float(analysis.get("durationSeconds", 0.0)) * float(analysis.get("bpm", 120.0)) / 60.0, "intensity": 0.5}] + overrides = {entry["id"]: entry for entry in plan["sectionOverrides"]} + style_scale = {"balanced": 1.0, "flow": 0.95, "tech": 1.05, "chill": 0.75}[plan["style"]] + planned = [] + for index, section in enumerate(source): + item = dict(section) + section_id = str(item.get("id") or f"section-{index:03d}") + label = str(item.get("label") or item.get("type") or "body") + family = re.sub(r"[\d\W_]+", "", label.casefold()) or "body" + chorus = any(word in family for word in ("chorus", "drop", "peak")) + verse = "verse" in family + factor = plan["chorusDensity"] if chorus else plan["verseDensity"] if verse else 1.0 + override = overrides.get(section_id, {}) + density = plan["density"] * factor * style_scale * override.get("density", 1.0) + item.update({ + "id": section_id, "label": label, "motifId": family, + "motifOffset": sum((i + 1) * ord(character) for i, character in enumerate(family)) % 4, + "density": round(max(0.35, min(1.75, density)), 6), + "intensity": max(0.0, min(1.0, float(item.get("intensity", 0.5)) * plan["intensity"] * override.get("intensity", 1.0))), + "dominantInstrument": override.get("dominantInstrument", plan["dominantInstrument"]), + "style": plan["style"], + }) + planned.append(item) + missing = set(overrides) - {item["id"] for item in planned} + if missing: + raise ValueError("unknown section ids: " + ", ".join(sorted(missing))) + return {"schemaVersion": 1, "source": sections.get("source", "internal"), "sections": planned, "controls": plan} diff --git a/skills/beat-saber-mapping/scripts/rl/README.md b/skills/beat-saber-mapping/scripts/rl/README.md new file mode 100644 index 0000000..2b121cf --- /dev/null +++ b/skills/beat-saber-mapping/scripts/rl/README.md @@ -0,0 +1,148 @@ +# RL training, evaluation, and checkpoint promotion + +RL remains experimental. The studio's constraint solver is the default. A saved +PPO reward is not proof of musical quality, and generated maps still require the +normal package validator and human headset checks. + +Set `PYTHONPATH` to `skills/beat-saber-mapping/scripts` and use the project's Python +environment. PyTorch is an optional dependency. These commands operate on local +files and do not download models, analyze new audio, or install songs. + +## Prepare a song library + +Run `analyze_audio.py` and resolve the normal timing gate first. Every song needs +`analysis.json`, `beat_grid.json`, `sections.json`, and `audio_features.npz`. +Training and evaluation require `timing_verified`. The studio's explicit +unconfirmed-generation override is the only caller that disables this check, +and its feature provenance continues to say `needs_anchors`. + +Use a manifest outside the repository for your private audio library: + +```json +{ + "schemaVersion": 1, + "tracks": [ + {"id": "training-song", "split": "train", "analysisDir": "analysis/training-song"}, + {"id": "validation-song", "split": "validation", "analysisDir": "analysis/validation-song"}, + {"id": "final-test-song", "split": "test", "analysisDir": "analysis/final-test-song"} + ] +} +``` + +Paths resolve relative to the manifest. IDs and analyzed audio SHA-256 hashes +must be unique across the entire library. Use varied songs and keep the final +test split untouched while selecting models. Audio, analysis, maps, checkpoints, +and evaluation outputs stay out of version control. + +## Train with the same inputs used at generation + +```text +python -m rl.train_custom_tracks --manifest library.json --rounds 1 --timesteps-per-track 16384 --seed 42 --out data/models/candidate.pt +python -m rl.train_ppo --analysis-dir analysis/song --timesteps 16384 --seed 42 --out data/models/single-song.pt +``` + +`--analysis-dir` can be repeated for library training without a manifest. +`--no-resume` explicitly starts fresh. Resuming a real-song run from a checkpoint +without recorded training hashes is rejected because held-out independence +cannot be established. Old `--ram-dir`, `--ninajirachi-dir`, and `--spotify-dir` +discovery was replaced by explicit artifact directories or a manifest. + +The training loop uses the requested number of transitions exactly. Full song +duration stays in the environment; a short requested training budget may still +cover only part of a song. The default is one library round. It no longer silently +performs 99 rounds. Seeds cover initialization, policy sampling, and batch order. +Repeatability applies to the same device and runtime versions. + +The second hand's action mask uses the first hand's selected note. Simultaneous +handclaps, intersecting saber paths, and center vision doubles are masked during +PPO collection and inference. PPO stores that conditional mask for replay, so +the action probability used during optimization matches collection. This mask +does not replace validation of holds, walls, bombs, and the final package. + +For a software smoke test, use the explicit `--synthetic` option on `train_ppo`. +That pulse has no separated stems or verified song timing and cannot establish +held-out song performance. + +`rl.features.load_analysis_features(path)` returns `AnalysisFeatures` with +`audio_features`, `beat_grid`, `bpm`, `provenance`, and the original `analysis`. +The schema is `beatforge-rl-sample-grid-v2`. Quarter-beat decision samples come +from the monotonic integer sample grid, including offsets and tempo changes. +Frame interpolation uses saved sample positions, seconds, or `hopSamples`. +`sections.json.sections` supplies interval labels. Sample and second boundaries +take precedence over derived beat fields. Real Demucs event `stemEnergy` +annotations enter their nearest decision step. Missing stems remain zero and +`stemAvailability` records which stems actually exist. Missing spectral flux is +zero with `fluxAvailable: false`; the mix onset envelope is never relabeled as a +separated instrument. The 69-value policy observation layout remains compatible; +availability flags and provenance are metadata, while section labels influence +the musical reward. + +Checkpoints contain `state_dict` and JSON-compatible `metadata`, including the +feature schema, seed, training audio hashes, and analysis provenance. Saves use +an atomic replacement. Inference uses `weights_only=True`, checks tensor shapes +and finite values, and records the exact checkpoint SHA-256. Legacy raw state +dicts can still be loaded explicitly for generation with +`featureSchema: legacy-unrecorded`. They cannot enter a verified held-out +comparison. A missing or incompatible checkpoint never falls back to a random +policy. Callers supplying a policy instance directly are marked +`source: supplied-policy`, for tests or explicit application integration. + +## Compare on held-out songs + +```text +python -m rl.evaluate --manifest library.json --checkpoint data/models/baseline.pt --checkpoint data/models/candidate.pt --split validation --difficulty Expert --seed 42 --out data/evaluations/comparison.json +``` + +The command checks checkpoint training hashes against held-out audio hashes, +generates each chart deterministically, projects it onto the exact audio clock, +and runs the shared chart validator. It reports hard errors, warning codes, +NPS, hand imbalance, repeated transitions, section density, and the mean onset +strength at note positions. Empty charts count as failures. The report includes +sample artifact hashes, checkpoint hashes, evaluator code hashes, runtime +versions, chart hashes, and the seed. Exit status 2 means a generated chart has +hard errors; the JSON report is still saved. + +These measurements do not replace packaged-map checks or human evaluation. +Blind preferences, manual corrections, and headset playtests are separate +evidence. No generated report claims those checks happened. + +## Promote and roll back + +```text +python -m rl.registry --registry data/model_registry initialize --checkpoint data/models/baseline.pt --evaluation data/evaluations/comparison.json +python -m rl.registry --registry data/model_registry promote --checkpoint data/models/candidate.pt --evaluation data/evaluations/comparison.json +python -m rl.registry --registry data/model_registry active +python -m rl.registry --registry data/model_registry rollback +``` + +Initialization explicitly selects a validated, nonempty baseline. Promotion +requires identical manifest, split, song analysis hashes, difficulty, seed, +evaluator code, feature schema, and runtime versions. Each candidate chart must +have zero hard errors and no increase in warnings, hand imbalance, repeated +transition fraction, or distance from the difficulty's target NPS. No held-out +song may regress in onset alignment, and the mean onset-strength gain must be +at least 0.001. This is a measured candidate improvement, not proof that humans +prefer it. If the benchmark or evaluator changes, evaluate both models again +and explicitly establish a new registry baseline. + +The registry copies checkpoints and evaluations into folders named by their +content hashes. `registry.json` contains `schemaVersion`, `active`, `models`, +and an append-only `history` of selections. Promotion atomically switches the +active reference and retains prior files for rollback. It does not delete or +overwrite the source checkpoints. Concurrent writes fail with a lock error. +`active_checkpoint(registry_root)` verifies the active file's hash before +returning its path. A feedback-triggered local job can call +`register_checkpoint(root, checkpoint, evaluation)` after an explicitly run +training/evaluation cycle; this module starts no background work. + +## Focused checks + +```text +python -m pytest skills/beat-saber-mapping/tests/test_rl_features.py skills/beat-saber-mapping/tests/test_rl_training_correctness.py skills/beat-saber-mapping/tests/test_rl_registry.py skills/beat-saber-mapping/tests/test_rl_environment.py +``` + +Tests cover offset and tempo-change sampling, localized real stems, missing +inputs, section intervals, train/eval content leakage, checkpoint corruption, +terminal PPO bootstrap boundaries, exact budgets, seeds, promotion rejection, +and artifact-preserving rollback. They use small synthetic fixtures rather than +training on or redistributing private song audio. diff --git a/skills/beat-saber-mapping/scripts/rl/__init__.py b/skills/beat-saber-mapping/scripts/rl/__init__.py index b1b74ac..5a22b08 100644 --- a/skills/beat-saber-mapping/scripts/rl/__init__.py +++ b/skills/beat-saber-mapping/scripts/rl/__init__.py @@ -2,9 +2,18 @@ from __future__ import annotations -from .environment import BeatSaberEnv -from .models import ActorCriticPolicy, BeatForgeAudioEncoder -from .rewards import CompositeReward, KinematicReward, MusicalReward, StyleReward +def __getattr__(name): + # Feature extraction and benchmark metric helpers work without PyTorch. + if name in {"ActorCriticPolicy", "BeatForgeAudioEncoder"}: + from . import models + return getattr(models, name) + if name == "BeatSaberEnv": + from .environment import BeatSaberEnv + return BeatSaberEnv + if name in {"CompositeReward", "KinematicReward", "MusicalReward", "StyleReward"}: + from . import rewards + return getattr(rewards, name) + raise AttributeError(name) __all__ = [ "BeatSaberEnv", diff --git a/skills/beat-saber-mapping/scripts/rl/checkpoints.py b/skills/beat-saber-mapping/scripts/rl/checkpoints.py new file mode 100644 index 0000000..bb3fe62 --- /dev/null +++ b/skills/beat-saber-mapping/scripts/rl/checkpoints.py @@ -0,0 +1,58 @@ +"""Validated policy weights and portable, non-pickle provenance metadata.""" + +from __future__ import annotations + +from pathlib import Path +import json +from typing import Any + +import torch + +from .features import FEATURE_SCHEMA, file_sha256 +from .models import ActorCriticPolicy + + +def load_checkpoint(path: Path, device: str = "cpu") -> tuple[ActorCriticPolicy, dict[str, Any]]: + path = Path(path) + if not path.is_file(): + raise FileNotFoundError(f"RL checkpoint does not exist: {path}") + try: + payload = torch.load(path, map_location=device, weights_only=True) + if not isinstance(payload, dict): + raise ValueError("checkpoint must contain policy weights") + wrapped = "state_dict" in payload + weights = payload["state_dict"] if wrapped else payload + metadata = dict(payload.get("metadata", {})) if wrapped else {"featureSchema": "legacy-unrecorded"} + json.dumps(metadata, allow_nan=False) + for name in ("trainingSourceSha256", "heldOutSourceSha256"): + if name in metadata: + hashes = metadata[name] + if (not isinstance(hashes, list) or not all(isinstance(value, str) and len(value) == 64 + and all(character in "0123456789abcdef" for character in value) for value in hashes)): + raise ValueError(f"Invalid checkpoint lineage field: {name}") + schema = metadata.get("featureSchema", "legacy-unrecorded") + if schema not in {FEATURE_SCHEMA, "legacy-unrecorded"}: + raise ValueError(f"unsupported feature schema: {schema}") + first = weights["encoder.0.weight"] + if first.ndim != 2 or first.shape[1] != 69: + raise ValueError("checkpoint observation dimensions are incompatible with the RL environment") + with torch.random.fork_rng(devices=[]): + policy = ActorCriticPolicy(obs_dim=int(first.shape[1]), hidden_dim=int(first.shape[0])) + if not all(isinstance(value, torch.Tensor) and torch.is_floating_point(value) and torch.isfinite(value).all() for value in weights.values()): + raise ValueError("checkpoint contains non-finite or invalid weights") + policy.load_state_dict(weights, strict=True) + except Exception as exc: + raise ValueError(f"Invalid RL checkpoint {path.name}: {exc}") from exc + policy.to(device) + policy.eval() + metadata.update({"checkpointFile": path.name, "checkpointSha256": file_sha256(path)}) + return policy, metadata + + +def save_checkpoint(policy: ActorCriticPolicy, path: Path, metadata: dict[str, Any]) -> None: + """Replace checkpoints atomically so interruption cannot destroy a prior save.""" + path = Path(path) + path.parent.mkdir(parents=True, exist_ok=True) + temporary = path.with_name(path.name + ".tmp") + torch.save({"state_dict": policy.state_dict(), "metadata": {**metadata, "featureSchema": FEATURE_SCHEMA}}, temporary) + temporary.replace(path) diff --git a/skills/beat-saber-mapping/scripts/rl/environment.py b/skills/beat-saber-mapping/scripts/rl/environment.py index db1d13f..4870237 100644 --- a/skills/beat-saber-mapping/scripts/rl/environment.py +++ b/skills/beat-saber-mapping/scripts/rl/environment.py @@ -21,6 +21,7 @@ vision_blocking_double, ) from safety_contract import RECOVERY_BEATS, occupied_until +from .safety import ValidationClock, transition_is_safe try: from .rewards import CompositeReward, RewardBreakdown except ImportError: @@ -71,10 +72,14 @@ def __init__( difficulty: str = "Expert", lookahead_steps: int = 4, ): - self.bpm = max(1.0, float(bpm)) + self.bpm = float(bpm) + if not math.isfinite(self.bpm) or self.bpm <= 0: + raise ValueError("RL BPM must be finite and positive") + self.base_bpm = self.bpm self.difficulty = difficulty if difficulty in DIFFICULTIES else "Expert" self.target_nps = TARGET_NPS_MAP.get(self.difficulty, 4.25) self.lookahead_steps = max(1, lookahead_steps) + self.rng = np.random.default_rng(0) self.reward_engine = CompositeReward( difficulty=self.difficulty, @@ -84,9 +89,10 @@ def __init__( # Internal state buffers self.beat_grid = self._normalize_beat_grid(beat_grid) if beat_grid is not None else self._generate_synthetic_grid(64, self.bpm) - self.audio_features = audio_features or self._generate_synthetic_audio(len(self.beat_grid)) + self.audio_features = audio_features if audio_features is not None else self._generate_synthetic_audio(len(self.beat_grid)) self.current_step = 0 self.total_steps = len(self.beat_grid) + self.validation_clock = ValidationClock(self.audio_features, self.beat_grid, self.base_bpm) # Kinematic hand states self.red_prev: Optional[Dict[str, Any]] = None @@ -106,31 +112,25 @@ def _normalize_beat_grid(self, beat_grid: Any) -> List[float]: if beat_grid is None: return self._generate_synthetic_grid(64, self.bpm) if isinstance(beat_grid, dict): - if "beats" in beat_grid and isinstance(beat_grid["beats"], list): - return [float(b) for b in beat_grid["beats"]] - if "tempoRegions" in beat_grid: - max_beat = max((float(r.get("endBeat", 0.0)) for r in beat_grid.get("tempoRegions", [])), default=64.0) - return [round(i * 0.25, 4) for i in range(max(16, int(max_beat * 4)))] - return self._generate_synthetic_grid(64, self.bpm) - return [float(b) for b in beat_grid] + raise ValueError("Use rl.features.load_analysis_features to project an analysis grid") + result = [float(b) for b in beat_grid] + if not result or not np.isfinite(result).all() or np.any(np.diff(result) <= 0): + raise ValueError("Environment beat grid must be nonempty, finite, and strictly increasing") + return result def _generate_synthetic_grid(self, num_beats: int, bpm: float) -> List[float]: # Quarter-beat subdivision return [round(i * 0.25, 4) for i in range(num_beats * 4)] def _generate_synthetic_audio(self, length: int) -> Dict[str, Any]: + stems = ("drums", "bass", "guitar", "piano", "vocals", "other") return { - "onsets": np.random.uniform(0.0, 1.0, size=(length,)).tolist(), - "flux": np.random.uniform(0.0, 1.0, size=(length,)).tolist(), - "stems": { - "drums": np.random.uniform(0.0, 1.0, size=(length,)).tolist(), - "bass": np.random.uniform(0.0, 1.0, size=(length,)).tolist(), - "guitar": np.random.uniform(0.0, 0.5, size=(length,)).tolist(), - "piano": np.random.uniform(0.0, 0.5, size=(length,)).tolist(), - "vocals": np.random.uniform(0.0, 0.8, size=(length,)).tolist(), - "other": np.random.uniform(0.0, 0.5, size=(length,)).tolist(), - }, - "sections": ["verse"] * length, + "onsets": [1.0 if i % 4 == 0 else 0.0 for i in range(length)], + "flux": [0.0] * length, + "stems": {stem: [0.0] * length for stem in stems}, + "stemAvailability": {stem: False for stem in stems}, + "sections": ["unknown"] * length, + "provenance": {"source": "synthetic-test-pulse", "timingStatus": "synthetic"}, } def reset( @@ -142,32 +142,34 @@ def reset( difficulty: Optional[str] = None, ) -> Tuple[np.ndarray, Dict[str, Any]]: if seed is not None: - np.random.seed(seed) + self.rng = np.random.default_rng(seed) if bpm is not None: - self.bpm = max(1.0, float(bpm)) + if not math.isfinite(float(bpm)) or float(bpm) <= 0: + raise ValueError("RL BPM must be finite and positive") + self.bpm = self.base_bpm = float(bpm) if difficulty is not None and difficulty in DIFFICULTIES: self.difficulty = difficulty self.target_nps = TARGET_NPS_MAP.get(self.difficulty, 4.25) - self.reward_engine = CompositeReward( - difficulty=self.difficulty, - bpm=self.bpm, - target_nps=self.target_nps, - ) + self.reward_engine = CompositeReward( + difficulty=self.difficulty, bpm=self.bpm, target_nps=self.target_nps, + ) if beat_grid is not None: self.beat_grid = self._normalize_beat_grid(beat_grid) elif audio_features is not None: - self.beat_grid = self._generate_synthetic_grid(len(audio_features.get("onsets", [])), self.bpm) + self.beat_grid = self._normalize_beat_grid([i * 0.25 for i in range(len(audio_features.get("onsets", [])))]) if audio_features is not None: self.audio_features = audio_features self.current_step = 0 self.total_steps = len(self.beat_grid) + self.validation_clock = ValidationClock(self.audio_features, self.beat_grid, self.base_bpm) self.red_prev = None self.blue_prev = None self.placed_notes = [] + self._update_step_tempo() obs = self._get_observation() info = {"current_step": 0, "beat": self.beat_grid[0] if self.beat_grid else 0.0} @@ -175,10 +177,12 @@ def reset( def action_masks(self) -> Tuple[np.ndarray, np.ndarray]: """Compute binary action masks for Left (Red) and Right (Blue) hands.""" + self._update_step_tempo() mask_red = np.ones(NUM_HAND_ACTIONS, dtype=bool) mask_blue = np.ones(NUM_HAND_ACTIONS, dtype=bool) current_beat = self.beat_grid[self.current_step] if self.current_step < self.total_steps else 0.0 + validation_beat = self.validation_clock.beat(current_beat) recovery_beats = RECOVERY_BEATS.get(self.difficulty, 0.30) # Rhythmic grid subdivision gating @@ -193,7 +197,7 @@ def action_masks(self) -> Tuple[np.ndarray, np.ndarray]: return mask_red, mask_blue # 1. Mask Left Hand (Red, Color 0) - if self.red_prev is not None and (current_beat - float(self.red_prev["b"])) < (recovery_beats - 1e-5): + if self.red_prev is not None and (validation_beat - self.validation_clock.beat(float(self.red_prev["b"]))) < recovery_beats: mask_red[1:] = False else: for act in range(1, NUM_HAND_ACTIONS): @@ -204,14 +208,11 @@ def action_masks(self) -> Tuple[np.ndarray, np.ndarray]: candidate = {"b": current_beat, "c": 0, "x": x, "y": y, "d": d} if self.red_prev is not None: - finding = transition_finding( - self.red_prev, candidate, self.bpm, recovery_beats=recovery_beats - ) - if finding is not None: + if not transition_is_safe(self.validation_clock.note(self.red_prev), self.validation_clock.note(candidate), self.base_bpm, self.difficulty): mask_red[act] = False # 2. Mask Right Hand (Blue, Color 1) - if self.blue_prev is not None and (current_beat - float(self.blue_prev["b"])) < (recovery_beats - 1e-5): + if self.blue_prev is not None and (validation_beat - self.validation_clock.beat(float(self.blue_prev["b"]))) < recovery_beats: mask_blue[1:] = False else: for act in range(1, NUM_HAND_ACTIONS): @@ -222,14 +223,27 @@ def action_masks(self) -> Tuple[np.ndarray, np.ndarray]: candidate = {"b": current_beat, "c": 1, "x": x, "y": y, "d": d} if self.blue_prev is not None: - finding = transition_finding( - self.blue_prev, candidate, self.bpm, recovery_beats=recovery_beats - ) - if finding is not None: + if not transition_is_safe(self.validation_clock.note(self.blue_prev), self.validation_clock.note(candidate), self.base_bpm, self.difficulty): mask_blue[act] = False return mask_red, mask_blue + def blue_action_mask(self, red_action: int, base_mask: Optional[np.ndarray] = None) -> np.ndarray: + """Condition the second hand on the first hand's actual selected action.""" + mask = (base_mask if base_mask is not None else self.action_masks()[1]).copy() + decoded = decode_hand_action(red_action) + if decoded is None or self.current_step >= self.total_steps: + return mask + beat = self.beat_grid[self.current_step] + red = {"b": beat, "c": 0, "x": decoded[0], "y": decoded[1], "d": decoded[2]} + for action in np.flatnonzero(mask[1:]) + 1: + x, y, direction = decode_hand_action(int(action)) + blue = {"b": beat, "c": 1, "x": x, "y": y, "d": direction} + if cross_hand_finding(red, blue) is not None or vision_blocking_double(red, blue): + mask[action] = False + mask[ACTION_IDLE] = True + return mask + def step( self, action: Tuple[int, int] ) -> Tuple[np.ndarray, float, bool, bool, Dict[str, Any]]: @@ -240,6 +254,7 @@ def step( act_red, act_blue = action current_beat = self.beat_grid[self.current_step] + self._update_step_tempo() # Decode actions red_decoded = decode_hand_action(act_red) @@ -269,16 +284,16 @@ def step( # Compute Step Reward breakdown: RewardBreakdown = self.reward_engine.compute_step_reward( - beat=current_beat, - red_prev=self.red_prev, - red_curr=red_curr, - blue_prev=self.blue_prev, - blue_curr=blue_curr, + beat=self.validation_clock.beat(current_beat), + red_prev=self.validation_clock.note(self.red_prev), + red_curr=self.validation_clock.note(red_curr), + blue_prev=self.validation_clock.note(self.blue_prev), + blue_curr=self.validation_clock.note(blue_curr), onset_strength=onset, stem_energy=stem_energy, is_downbeat=is_downbeat, section_type=section_type, - recent_notes=self.placed_notes[-self.history_window:], + recent_notes=[self.validation_clock.note(note) for note in self.placed_notes[-self.history_window:]], current_nps=current_nps, target_nps=self.target_nps, ) @@ -292,6 +307,7 @@ def step( self.blue_prev = blue_curr self.current_step += 1 + self._update_step_tempo() terminated = self.current_step >= self.total_steps truncated = False @@ -310,6 +326,13 @@ def _get_audio_field(self, field: str, idx: int, default: Any) -> Any: return arr[idx] return default + def _update_step_tempo(self) -> None: + if not self.total_steps: + return + value = self._get_audio_field("stepBpms", min(self.current_step, self.total_steps - 1), self.bpm) + self.bpm = float(value) + self.reward_engine.kinematic.bpm = self.base_bpm + def _get_stem_energy(self, stem: str, idx: int) -> float: stems = self.audio_features.get("stems", {}) arr = stems.get(stem) @@ -324,7 +347,11 @@ def _compute_rolling_nps(self) -> float: span_beats = float(recent[-1]["b"]) - float(recent[0]["b"]) if span_beats <= 1e-6: return 0.0 - span_seconds = span_beats * 60.0 / self.bpm + times = self.audio_features.get("timesSeconds") + if times is not None and len(times) == self.total_steps: + span_seconds = float(np.interp(float(recent[-1]["b"]), self.beat_grid, times) - np.interp(float(recent[0]["b"]), self.beat_grid, times)) + else: + span_seconds = span_beats * 60.0 / self.bpm return len(recent) / max(0.1, span_seconds) def _get_observation(self) -> np.ndarray: diff --git a/skills/beat-saber-mapping/scripts/rl/evaluate.py b/skills/beat-saber-mapping/scripts/rl/evaluate.py new file mode 100644 index 0000000..e11683f --- /dev/null +++ b/skills/beat-saber-mapping/scripts/rl/evaluate.py @@ -0,0 +1,142 @@ +#!/usr/bin/env python3 +"""Offline held-out checkpoint evaluation. No downloads, training, or installation.""" + +from __future__ import annotations + +import argparse +from collections import Counter +from datetime import datetime, timezone +import json +import hashlib +from pathlib import Path +from typing import Any + +import numpy as np + +from beatforge_core import ValidationReport +from validate_map import validate_v3 +from timing_export import project_map_timing +from rl.features import AnalysisFeatures, FEATURE_SCHEMA, file_sha256 +from rl.library import load_library + + +def chart_metrics(chart: dict[str, Any], bundle: AnalysisFeatures, difficulty: str) -> dict[str, Any]: + """Report measurable chart properties, without assigning a subjective score.""" + report = ValidationReport() + exported = project_map_timing(chart, bundle.analysis) + structural = validate_v3(exported, f"{difficulty}Standard.dat", report, bpm=bundle.bpm, difficulty=difficulty) + notes = sorted(chart.get("colorNotes", []), key=lambda note: (note["b"], note["c"])) + if not notes: + report.add("error", "EMPTY_CHART", "A playable chart must contain color notes") + duration = bundle.provenance["durationSamples"] / bundle.provenance["sampleRate"] + hand_counts = Counter(int(note["c"]) for note in notes) + transitions = [] + for color in (0, 1): + hand = [note for note in notes if note["c"] == color] + transitions.extend((left["x"], left["y"], left["d"], right["x"], right["y"], right["d"]) + for left, right in zip(hand, hand[1:])) + pattern_counts = Counter(transitions) + labels = bundle.audio_features["sections"] + times = bundle.audio_features["timesSeconds"] + note_onsets = [] + section_counts: Counter[str] = Counter() + for note in notes: + index = int(np.clip(np.searchsorted(bundle.beat_grid, note["b"]), 0, len(labels) - 1)) + section_counts[labels[index]] += 1 + note_onsets.append(bundle.audio_features["onsets"][index]) + section_seconds: Counter[str] = Counter() + for index, label in enumerate(labels): + end = times[index + 1] if index + 1 < len(times) else duration + section_seconds[label] += max(0.0, end - times[index]) + return { + "notes": len(notes), "nps": len(notes) / duration, + "redNotes": hand_counts[0], "blueNotes": hand_counts[1], + "handImbalance": abs(hand_counts[0] - hand_counts[1]) / max(1, len(notes)), + "mostRepeatedTransitionFraction": max(pattern_counts.values(), default=0) / max(1, len(transitions)), + "meanOnsetStrengthAtNotes": float(np.mean(note_onsets)) if note_onsets else 0.0, + "sectionNps": {label: section_counts[label] / seconds for label, seconds in section_seconds.items() if seconds > 0}, + "hardErrors": len(report.errors), "warnings": len(report.warnings), + "errorCodes": dict(Counter(issue.code for issue in report.errors)), + "structural": structural, + "headsetPlaytest": "not_performed", + } + + +def evaluate_checkpoints( + manifest: Path, checkpoints: list[Path], *, split: str = "validation", + difficulty: str = "Expert", seed: int = 0, +) -> dict[str, Any]: + from rl.policy_generator import RLMapGenerator + import torch + + if split not in {"validation", "test"}: + raise ValueError("Evaluation must use a held-out validation or test split") + library = load_library(manifest) + held_out = [(track, bundle) for track, bundle in library if track["split"] == split] + if not held_out: + raise ValueError(f"Manifest has no {split} tracks") + results = [] + for checkpoint in checkpoints: + generator = RLMapGenerator(model_path=Path(checkpoint)) + training = generator.checkpoint_metadata.get("trainingSourceSha256") + if not training: + raise ValueError("Checkpoint has no training audio hashes; held-out independence cannot be verified") + if generator.checkpoint_metadata.get("featureSchema") != FEATURE_SCHEMA: + raise ValueError("Held-out comparison requires the current shared feature schema") + if set(training) & {bundle.provenance["sourceSha256"] for _, bundle in held_out}: + raise ValueError("Held-out split contains audio used to train this checkpoint") + tracks = [] + for track, bundle in held_out: + chart = generator.generate_difficulty(bundle.audio_features, bundle.beat_grid, bundle.bpm, + difficulty=difficulty, deterministic=True, seed=seed) + tracks.append({"id": track["id"], "features": bundle.provenance, + "chartSha256": hashlib.sha256(json.dumps(chart, sort_keys=True).encode()).hexdigest(), + "metrics": chart_metrics(chart, bundle, difficulty)}) + results.append({ + "checkpoint": generator.checkpoint_metadata, "tracks": tracks, + "summary": {"tracks": len(tracks), "hardErrors": sum(t["metrics"]["hardErrors"] for t in tracks), + "meanNps": float(np.mean([t["metrics"]["nps"] for t in tracks])), + "tracksWithoutHardErrors": sum(t["metrics"]["hardErrors"] == 0 for t in tracks)}, + }) + return { + "schemaVersion": 1, "kind": "offline-held-out-checkpoint-evaluation", + "createdAt": datetime.now(timezone.utc).isoformat(), "seed": seed, + "deterministic": True, "split": split, "difficulty": difficulty, + "manifestSha256": file_sha256(manifest), "featureSchema": FEATURE_SCHEMA, + "runtime": {"torch": str(torch.__version__), "numpy": str(np.__version__)}, + "evaluatorSha256": { + name: file_sha256(Path(__file__).parent / name) + for name in ("evaluate.py", "features.py", "environment.py", "policy_generator.py", "models.py", "rewards.py", "safety.py") + } | { + name: file_sha256(Path(__file__).parent.parent / name) + for name in ("validate_map.py", "kinematics.py", "safety_contract.py", "timing_export.py", "beatforge_core.py") + }, + "heldOutVerifiedAgainstRecordedTraining": True, + "limitations": ["Recorded hashes establish data separation; they do not prove prior undocumented training history.", + "Chart validation does not replace a complete packaged-map or headset playtest.", + "Musical preference and manual correction counts require human evaluation."], + "results": results, + } + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--manifest", type=Path, required=True) + parser.add_argument("--checkpoint", type=Path, action="append", required=True) + parser.add_argument("--split", choices=("validation", "test"), default="validation") + parser.add_argument("--difficulty", choices=("Easy", "Normal", "Hard", "Expert", "ExpertPlus"), default="Expert") + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--out", type=Path, required=True) + args = parser.parse_args() + try: + result = evaluate_checkpoints(args.manifest, args.checkpoint, split=args.split, difficulty=args.difficulty, seed=args.seed) + except (ValueError, FileNotFoundError) as exc: + parser.error(str(exc)) + args.out.parent.mkdir(parents=True, exist_ok=True) + args.out.write_text(json.dumps(result, indent=2, allow_nan=False) + "\n", encoding="utf-8") + print(f"Saved {len(result['results'])} checkpoint evaluation(s) to {args.out}") + return 2 if any(item["summary"]["hardErrors"] for item in result["results"]) else 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/skills/beat-saber-mapping/scripts/rl/features.py b/skills/beat-saber-mapping/scripts/rl/features.py new file mode 100644 index 0000000..8b418d7 --- /dev/null +++ b/skills/beat-saber-mapping/scripts/rl/features.py @@ -0,0 +1,219 @@ +"""One sample-aligned feature contract for RL training, evaluation, and generation. + +Consumes the existing analyzer artifacts. It never estimates or verifies timing. +Missing separated stems are zeros with availability recorded, never mix copies. +""" + +from __future__ import annotations + +from dataclasses import dataclass +import hashlib +import json +import math +from pathlib import Path +from typing import Any + +import numpy as np + +FEATURE_SCHEMA = "beatforge-rl-sample-grid-v2" +STEMS = ("drums", "bass", "guitar", "piano", "vocals", "other") + + +@dataclass +class AnalysisFeatures: + audio_features: dict[str, Any] + beat_grid: list[float] + bpm: float + provenance: dict[str, Any] + analysis: dict[str, Any] + + +def file_sha256(path: Path) -> str: + digest = hashlib.sha256() + with path.open("rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + digest.update(chunk) + return digest.hexdigest() + + +def _unit(values: np.ndarray) -> np.ndarray: + values = np.maximum(values, 0.0) + scale = max(1.0, float(np.max(values))) if values.size else 1.0 + return np.clip(values / scale, 0.0, 1.0) + + +def build_analysis_features( + analysis: dict[str, Any], + grid_document: dict[str, Any], + sections_document: dict[str, Any], + frames: dict[str, Any], + *, + require_verified: bool = True, +) -> AnalysisFeatures: + """Project frames and onset stem annotations onto actual quarter-beat samples.""" + if not all(isinstance(document, dict) for document in (analysis, grid_document, sections_document, frames)): + raise ValueError("RL analysis artifacts must contain objects") + status = str(analysis.get("status", "unknown")) + if require_verified and status != "timing_verified": + raise ValueError(f"RL requires verified analysis; timing status is {status}") + if grid_document.get("status", status) != status: + raise ValueError("Analysis and beat grid timing statuses disagree") + sample_rate = int(analysis["sampleRate"]) + if float(analysis["sampleRate"]) != sample_rate or float(grid_document.get("sampleRate", sample_rate)) != sample_rate: + raise ValueError("Analysis sample rate must be an integer") + if sample_rate != 44100 or int(grid_document.get("sampleRate", sample_rate)) != sample_rate: + raise ValueError("RL analysis must use the canonical 44100 Hz sample clock") + duration = int(analysis["durationSamples"]) + if float(analysis["durationSamples"]) != duration: + raise ValueError("Analysis durationSamples must be an integer") + bpm = float(analysis["bpm"]) + if duration <= 0 or not math.isfinite(bpm) or bpm <= 0: + raise ValueError("Analysis has invalid duration or BPM") + rows = grid_document.get("beats", []) + if len(rows) < 2 or not all(isinstance(row, dict) for row in rows): + raise ValueError("beat_grid.json must contain at least two beat/sample rows") + beats = np.asarray([row["beat"] for row in rows], dtype=float) + raw_samples = np.asarray([row["sample"] for row in rows], dtype=float) + if (not np.isfinite(beats).all() or not np.isfinite(raw_samples).all() + or np.any(raw_samples != np.round(raw_samples)) + or np.any(np.diff(beats) <= 0) or np.any(np.diff(raw_samples) <= 0)): + raise ValueError("Beat/sample rows must be finite, integer-sampled, and strictly increasing") + if raw_samples[0] < 0 or raw_samples[-1] >= duration: + raise ValueError("Beat samples fall outside the analyzed audio") + if "beatGrid" in analysis: + recorded = [(row["beat"], row["sample"]) for row in analysis["beatGrid"]] + if recorded != [(row["beat"], row["sample"]) for row in rows]: + raise ValueError("Analysis and beat_grid.json contain different adopted timing grids") + + # The analyzer may provide finer subdivisions. Select the quarter-beat domain + # through interpolation in the adopted sample clock, including pickups. + first_tick = math.ceil(beats[0] * 4 - 1e-7) + last_tick = math.floor(beats[-1] * 4 + 1e-7) + steps = np.arange(first_tick, last_tick + 1, dtype=float) / 4.0 + if not len(steps): + raise ValueError("Beat grid contains no quarter-beat decision steps") + samples = np.rint(np.interp(steps, beats, raw_samples)).astype(np.int64) + if np.any(np.diff(samples) <= 0): + raise ValueError("Quarter-beat sample positions must increase") + times = samples / sample_rate + + def frame_values(key: str) -> np.ndarray: + values = np.asarray(frames.get(key, []), dtype=float) + if values.ndim != 1: + raise ValueError(f"Audio feature {key} must be a one-dimensional envelope") + if not np.isfinite(values).all(): + raise ValueError(f"Non-finite audio feature {key}") + return values + + def align(key: str) -> list[float]: + values = frame_values(key) + if not len(values): + return [0.0] * len(steps) + if "samples" in frames: + positions = np.asarray(frames["samples"], dtype=float).reshape(-1) + if np.any(positions != np.round(positions)): + raise ValueError("Audio frame sample positions must be integers") + elif "times" in frames or "timeSeconds" in frames: + positions = np.asarray(frames.get("times", frames.get("timeSeconds")), dtype=float).reshape(-1) * sample_rate + else: + hops = np.asarray(frames.get("hopSamples", frames.get("hop", 0))).reshape(-1) + if len(hops) != 1: + raise ValueError("Audio feature frames require a single hopSamples value") + hop = float(hops[0]) + if not math.isfinite(hop) or hop <= 0 or hop != round(hop): + raise ValueError("Audio feature frames require sample positions, times, or integer hopSamples") + positions = np.arange(len(values)) * hop + if len(positions) != len(values) or not np.isfinite(positions).all() or np.any(np.diff(positions) <= 0): + raise ValueError(f"Invalid frame clock for {key}") + return np.interp(samples, positions, _unit(values), left=0.0, right=0.0).tolist() + + onset_key = next((key for key in ("combinedOnset", "combined", "onsets") if key in frames), None) + if onset_key is None: + raise ValueError("Audio feature artifact is missing its onset envelope") + if not len(frame_values(onset_key)): + raise ValueError("Audio feature artifact contains an empty onset envelope") + onsets = align(onset_key) + flux_available = "flux" in frames and bool(len(frame_values("flux"))) + flux = align("flux") if flux_available else [0.0] * len(steps) + + stems = {stem: [0.0] * len(steps) for stem in STEMS} + available = {stem: False for stem in STEMS} + stem_sources: dict[str, str] = {} + sustains = [0.0] * len(steps) + # Demucs is persisted as event annotations after temporary WAVs are removed. + # Assign an event to its nearest step only; never extend it across silence. + for event in analysis.get("events", []): + energies = event.get("stemEnergy", {}) + sample_value = float(event["sample"]) + if not math.isfinite(sample_value) or sample_value != round(sample_value) or not 0 <= sample_value < duration: + raise ValueError("Analysis onset samples must be integers within the audio") + sample = int(sample_value) + right = min(int(np.searchsorted(samples, sample)), len(samples) - 1) + left = max(0, right - 1) + index = left if abs(int(samples[left]) - sample) <= abs(int(samples[right]) - sample) else right + sustain = float(event.get("sustainBeats", 0.0)) + if not math.isfinite(sustain) or sustain < 0: + raise ValueError("Analysis sustain duration must be finite and nonnegative") + sustains[index] = max(sustains[index], sustain) + for stem in STEMS: + if stem not in energies: + continue + value = float(energies[stem]) + if not math.isfinite(value) or value < 0: + raise ValueError(f"Invalid separated {stem} energy") + available[stem] = True + stem_sources[stem] = "analysis.events.stemEnergy" + stems[stem][index] = max(stems[stem][index], min(value, 1.0)) + + labels = ["unknown"] * len(steps) + for section in sections_document.get("sections", []): + # External segment beat fields may have used a constant BPM conversion. + # Prefer sample/second boundaries to retain offset and tempo changes. + if "startSample" in section and "endSample" in section: + selected = (samples >= int(section["startSample"])) & (samples < int(section["endSample"])) + elif "startSeconds" in section and "endSeconds" in section: + selected = (times >= float(section["startSeconds"])) & (times < float(section["endSeconds"])) + elif "startBeat" in section and "endBeat" in section: + selected = (steps >= float(section["startBeat"])) & (steps < float(section["endBeat"])) + else: + raise ValueError("Section requires a start/end interval") + label = str(section.get("label", section.get("type", "unknown"))) + for index in np.flatnonzero(selected): + labels[int(index)] = label + + intervals = np.searchsorted(beats, steps, side="right") - 1 + intervals = np.clip(intervals, 0, len(beats) - 2) + bpms = 60.0 * sample_rate * np.diff(beats)[intervals] / np.diff(raw_samples)[intervals] + provenance = { + "featureSchema": FEATURE_SCHEMA, "timingStatus": status, + "gridSource": grid_document.get("source", analysis.get("gridSource", "unknown")), + "sourceSha256": analysis.get("sourceSha256"), "sampleRate": sample_rate, + "durationSamples": duration, "decisionSteps": len(steps), + "firstSample": int(samples[0]), "lastSample": int(samples[-1]), + "stemAvailability": available, "stemSources": stem_sources, + "fluxAvailable": flux_available, + "sourceAudioVerified": False, + "sectionSource": sections_document.get("source", "analysis"), + } + audio_features = { + "onsets": onsets, "flux": flux, "stems": stems, "sections": labels, + "sustainBeats": sustains, + "samplePositions": samples.tolist(), "timesSeconds": times.tolist(), + "stepBpms": bpms.tolist(), "stemAvailability": available, + "provenance": provenance, + } + return AnalysisFeatures(audio_features, steps.tolist(), bpm, provenance, {**analysis, "beatGrid": rows}) + + +def load_analysis_features(analysis_dir: Path, *, require_verified: bool = True) -> AnalysisFeatures: + analysis_dir = Path(analysis_dir) + names = ("analysis.json", "beat_grid.json", "sections.json", "audio_features.npz") + for name in names: + if not (analysis_dir / name).is_file(): + raise FileNotFoundError(f"Missing RL analysis artifact: {analysis_dir / name}. Run analyze_audio.py first.") + documents = [json.loads((analysis_dir / name).read_text(encoding="utf-8")) for name in names[:3]] + with np.load(analysis_dir / names[3], allow_pickle=False) as archive: + frames = {key: archive[key] for key in archive.files} + result = build_analysis_features(*documents, frames, require_verified=require_verified) + result.provenance["artifactSha256"] = {name: file_sha256(analysis_dir / name) for name in names} + return result diff --git a/skills/beat-saber-mapping/scripts/rl/library.py b/skills/beat-saber-mapping/scripts/rl/library.py new file mode 100644 index 0000000..153b4f1 --- /dev/null +++ b/skills/beat-saber-mapping/scripts/rl/library.py @@ -0,0 +1,46 @@ +"""Explicit training and held-out song lists, identified by audio content hash.""" + +from __future__ import annotations + +import json +from pathlib import Path +from typing import Any + +from .features import AnalysisFeatures, file_sha256, load_analysis_features + + +def load_library(manifest: Path) -> list[tuple[dict[str, Any], AnalysisFeatures]]: + manifest = Path(manifest).resolve() + document = json.loads(manifest.read_text(encoding="utf-8")) + if document.get("schemaVersion") != 1 or not isinstance(document.get("tracks"), list): + raise ValueError("Library manifest requires schemaVersion 1 and a tracks list") + result = [] + identities: set[str] = set() + hashes: set[str] = set() + for track in document["tracks"]: + identity = str(track["id"]) + if identity in identities: + raise ValueError(f"Duplicate track ID: {identity}") + if track.get("split") not in {"train", "validation", "test"}: + raise ValueError(f"Track {identity} requires split train, validation, or test") + path = Path(track["analysisDir"]) + path = path if path.is_absolute() else manifest.parent / path + bundle = load_analysis_features(path) + source = bundle.provenance.get("sourceSha256") + if not isinstance(source, str) or len(source) != 64 or any(c not in "0123456789abcdef" for c in source.lower()): + raise ValueError(f"Track {identity} requires an analyzed audio SHA-256") + source = source.lower() + if track.get("audioPath"): + audio = Path(track["audioPath"]) + audio = audio if audio.is_absolute() else manifest.parent / audio + if file_sha256(audio) != source: + raise ValueError(f"Audio content changed since analysis: {identity}") + bundle.provenance["sourceAudioVerified"] = True + if source in hashes: + raise ValueError(f"Duplicate audio content or split leakage: {identity}") + hashes.add(source) + identities.add(identity) + result.append(({**track, "analysisDir": str(path.resolve())}, bundle)) + if not result: + raise ValueError("Library manifest contains no tracks") + return result diff --git a/skills/beat-saber-mapping/scripts/rl/models.py b/skills/beat-saber-mapping/scripts/rl/models.py index 187352b..1b17b8e 100644 --- a/skills/beat-saber-mapping/scripts/rl/models.py +++ b/skills/beat-saber-mapping/scripts/rl/models.py @@ -3,7 +3,7 @@ from __future__ import annotations -from typing import Dict, List, Optional, Tuple +from typing import Callable, Dict, List, Optional, Tuple import torch import torch.nn as nn @@ -103,6 +103,7 @@ def get_action_and_value( action: Optional[Tuple[torch.Tensor, torch.Tensor]] = None, mask_red: Optional[torch.Tensor] = None, mask_blue: Optional[torch.Tensor] = None, + blue_mask_fn: Optional[Callable[[torch.Tensor], torch.Tensor]] = None, ) -> Tuple[Tuple[torch.Tensor, torch.Tensor], torch.Tensor, torch.Tensor, torch.Tensor]: """Compute action sampling, log probability, entropy, and state value.""" features = self.encoder(obs) @@ -125,6 +126,9 @@ def get_action_and_value( logits_blue = self.actor_blue(blue_in) # Apply Action Masking for Blue Hand + if blue_mask_fn is not None: + conditional_mask = blue_mask_fn(act_red) + mask_blue = conditional_mask if mask_blue is None else mask_blue & conditional_mask if mask_blue is not None: logits_blue = torch.where(mask_blue, logits_blue, torch.tensor(-1e8, device=logits_blue.device)) @@ -147,6 +151,8 @@ def predict( mask_red: Optional[torch.Tensor] = None, mask_blue: Optional[torch.Tensor] = None, deterministic: bool = False, + blue_mask_fn: Optional[Callable[[torch.Tensor], torch.Tensor]] = None, + generator: Optional[torch.Generator] = None, ) -> Tuple[int, int]: """Inference action selection.""" self.eval() @@ -166,19 +172,20 @@ def predict( if deterministic: act_red = torch.argmax(logits_red, dim=-1) else: - dist_red = Categorical(logits=logits_red) - act_red = dist_red.sample() + act_red = torch.multinomial(torch.softmax(logits_red, dim=-1), 1, generator=generator).squeeze(-1) red_emb = self.red_action_embed(act_red) blue_in = torch.cat([features, red_emb], dim=-1) logits_blue = self.actor_blue(blue_in) + if blue_mask_fn is not None: + conditional_mask = blue_mask_fn(act_red) + mask_blue = conditional_mask if mask_blue is None else mask_blue & conditional_mask if mask_blue is not None: logits_blue = torch.where(mask_blue, logits_blue, torch.tensor(-1e8, device=logits_blue.device)) if deterministic: act_blue = torch.argmax(logits_blue, dim=-1) else: - dist_blue = Categorical(logits=logits_blue) - act_blue = dist_blue.sample() + act_blue = torch.multinomial(torch.softmax(logits_blue, dim=-1), 1, generator=generator).squeeze(-1) return int(act_red.item()), int(act_blue.item()) diff --git a/skills/beat-saber-mapping/scripts/rl/policy_generator.py b/skills/beat-saber-mapping/scripts/rl/policy_generator.py index ae83cfe..78e9fff 100644 --- a/skills/beat-saber-mapping/scripts/rl/policy_generator.py +++ b/skills/beat-saber-mapping/scripts/rl/policy_generator.py @@ -10,6 +10,9 @@ import numpy as np import torch +from .checkpoints import load_checkpoint +from .safety import safe_hold_candidates +from safety_contract import MIN_CHAIN_DURATION_BEATS try: from .environment import ( @@ -40,11 +43,11 @@ def __init__( self.device = device if policy is not None: self.policy = policy - elif model_path is not None and model_path.exists(): - self.policy = ActorCriticPolicy() - self.policy.load_state_dict(torch.load(model_path, map_location=device)) + self.checkpoint_metadata = {"source": "supplied-policy", "checkpointSha256": None} + elif model_path is not None: + self.policy, self.checkpoint_metadata = load_checkpoint(model_path, device) else: - self.policy = ActorCriticPolicy() + raise ValueError("RL generation requires an explicit trained checkpoint (--rl-model)") self.policy.to(device) self.policy.eval() @@ -56,6 +59,7 @@ def generate_difficulty( bpm: float, difficulty: str = "Expert", deterministic: bool = True, + seed: int = 0, ) -> Dict[str, Any]: """Generate a complete v3.3.0 difficulty map using the RL policy.""" env = BeatSaberEnv( @@ -64,7 +68,8 @@ def generate_difficulty( bpm=bpm, difficulty=difficulty, ) - obs, _ = env.reset() + random = torch.Generator(device=self.device).manual_seed(seed) + obs, _ = env.reset(seed=seed) color_notes: List[Dict[str, Any]] = [] bomb_notes: List[Dict[str, Any]] = [] @@ -80,7 +85,11 @@ def generate_difficulty( t_mask_b = torch.tensor(mask_blue, dtype=torch.bool, device=self.device).unsqueeze(0) act_red, act_blue = self.policy.predict( - t_obs, mask_red=t_mask_r, mask_blue=t_mask_b, deterministic=deterministic + t_obs, mask_red=t_mask_r, mask_blue=t_mask_b, deterministic=deterministic, + blue_mask_fn=lambda selected: torch.as_tensor( + env.blue_action_mask(int(selected.item()), mask_blue), dtype=torch.bool, device=self.device + ).unsqueeze(0), + generator=random, ) current_beat = round(beat_grid[step], 4) @@ -116,10 +125,13 @@ def generate_difficulty( break # Generate Arcs (Sliders) connecting smooth swing trajectories (Post-2022 v3 feature) - sliders = self._generate_arcs(color_notes) + sliders = self._generate_arcs(color_notes, audio_features, beat_grid) # Generate Chains (Burst Sliders) on sustained vibration onsets (Post-2022 v3 feature) burst_sliders = self._generate_chains(color_notes, audio_features, beat_grid) + sliders, burst_sliders, hold_relaxations = safe_hold_candidates( + color_notes, sliders, burst_sliders, audio_features, beat_grid, bpm, difficulty + ) # Generate framing walls & bombs obstacles, bomb_notes = self._generate_obstacles_and_bombs( @@ -150,11 +162,16 @@ def generate_difficulty( "_provenance": { "poseSolver": "rl-policy-v1", "format": "v3.3.0", + "seed": seed, + "checkpoint": self.checkpoint_metadata, + "features": audio_features.get("provenance", {}), + "holdRelaxations": hold_relaxations, + "holdSelection": "audio-evidence-with-fixed-note-safety", }, }, } - def _generate_arcs(self, color_notes: List[Dict[str, Any]]) -> List[Dict[str, Any]]: + def _generate_arcs(self, color_notes: List[Dict[str, Any]], audio_features: Dict[str, Any], beat_grid: List[float]) -> List[Dict[str, Any]]: """Generate rich v3 Arcs / Sliders connecting held phrases and melodic transition swings.""" arcs = [] by_color: Dict[int, List[Dict[str, Any]]] = {0: [], 1: []} @@ -166,9 +183,12 @@ def _generate_arcs(self, color_notes: List[Dict[str, Any]]) -> List[Dict[str, An head = notes[i] tail = notes[i + 1] gap = float(tail["b"]) - float(head["b"]) + index = int(np.searchsorted(beat_grid, float(head["b"]))) + sustains = audio_features.get("sustainBeats", []) + sustained = index < len(sustains) and float(sustains[index]) >= gap # Connect notes spaced 0.5 to 3.5 beats apart on melodic transitions - if 0.5 <= gap <= 3.5 and head["d"] != 8 and tail["d"] != 8 and (i % 2 == 0): + if sustained and 0.5 <= gap <= 3.5 and head["d"] != 8 and tail["d"] != 8 and (i % 2 == 0): control = min(2.0, max(0.8, gap * 0.45)) arcs.append({ "b": float(head["b"]), @@ -216,7 +236,7 @@ def _generate_chains( last_chain_beat = -999.0 for i, note in enumerate(color_notes): b = float(note["b"]) - step_idx = min(int(b * 4), len(onsets) - 1) + step_idx = int(np.searchsorted(beat_grid, b)) drum_val = float(drums[step_idx]) if step_idx >= 0 and step_idx < len(drums) and isinstance(drums, list) else 0.0 vocal_val = float(vocals[step_idx]) if step_idx >= 0 and step_idx < len(vocals) and isinstance(vocals, list) else 0.0 @@ -246,8 +266,8 @@ def _generate_chains( ty = min(2, max(0, hy + chosen_dy)) # Determine duration and slice count - dt = 0.0625 if (tx == hx and ty == hy) else (0.125 if abs(chosen_dx) + abs(chosen_dy) <= 1 else 0.25) - sc = 3 if dt <= 0.0625 else (4 if dt <= 0.125 else 5) + dt = MIN_CHAIN_DURATION_BEATS + sc = 5 chains.append({ "b": b, @@ -309,7 +329,7 @@ def notes_in_window(start_b: float, end_b: float) -> List[Dict[str, Any]]: if beat < last_wall_end + min_wall_gap: continue - step_idx = min(int(beat * 4), len(onsets) - 1) + step_idx = i intensity = onsets[step_idx] if 0 <= step_idx < len(onsets) else 0.5 bass_val = bass_energy[step_idx] if 0 <= step_idx < len(bass_energy) else 0.5 diff --git a/skills/beat-saber-mapping/scripts/rl/registry.py b/skills/beat-saber-mapping/scripts/rl/registry.py new file mode 100644 index 0000000..2122446 --- /dev/null +++ b/skills/beat-saber-mapping/scripts/rl/registry.py @@ -0,0 +1,273 @@ +"""Local checkpoint promotion with measured regression gates and rollback. + +Promotion never trains, downloads, installs maps, or asserts human preference. +Stored checkpoints and reports remain immutable by content hash. +""" + +from __future__ import annotations + +import argparse +from contextlib import contextmanager +from datetime import datetime, timezone +import json +import math +import os +from pathlib import Path +import shutil +from typing import Any + +from rl.environment import TARGET_NPS_MAP +from rl.features import FEATURE_SCHEMA, file_sha256 + +PRIMARY_METRIC = "meanOnsetStrengthAtNotes" +MIN_PRIMARY_GAIN = 0.001 + + +def _read(path: Path) -> dict[str, Any]: + return json.loads(path.read_text(encoding="utf-8")) + + +def _finite(metrics: dict[str, Any], key: str) -> float: + value = metrics.get(key) + if isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value): + raise ValueError(f"Evaluation is missing a finite metric: {key}") + return float(value) + + +def _result(report: dict[str, Any], checkpoint_hash: str) -> dict[str, Any]: + if (report.get("kind") != "offline-held-out-checkpoint-evaluation" + or report.get("schemaVersion") != 1 + or report.get("featureSchema") != FEATURE_SCHEMA + or report.get("heldOutVerifiedAgainstRecordedTraining") is not True + or report.get("split") not in {"validation", "test"} + or report.get("deterministic") is not True): + raise ValueError("Promotion requires a verified held-out evaluation with the current schema") + matches = [item for item in report.get("results", []) + if item.get("checkpoint", {}).get("checkpointSha256") == checkpoint_hash] + if len(matches) != 1: + raise ValueError("Evaluation must identify the exact candidate checkpoint once") + selected = matches[0] + training = selected.get("checkpoint", {}).get("trainingSourceSha256") + if not training or not selected.get("tracks"): + raise ValueError("Evaluation must record training hashes and nonempty held-out tracks") + seen: set[str] = set() + for track in selected["tracks"]: + if track["id"] in seen: + raise ValueError("Evaluation repeats a held-out track ID") + seen.add(track["id"]) + features = track.get("features", {}) + source = features.get("sourceSha256") + if not source or source in training or not features.get("artifactSha256"): + raise ValueError("Evaluation contains training overlap or lacks sample artifact identity") + if features.get("timingStatus") != "timing_verified": + raise ValueError("Promotion requires verified timing on every held-out track") + metrics = track["metrics"] + if _finite(metrics, "hardErrors") != 0: + raise ValueError(f"Candidate has hard validation errors on {track['id']}") + if _finite(metrics, "notes") <= 0: + raise ValueError(f"Candidate has an empty chart on {track['id']}") + for key in (PRIMARY_METRIC, "handImbalance", "mostRepeatedTransitionFraction", "nps", "warnings"): + if _finite(metrics, key) < 0: + raise ValueError(f"Metric cannot be negative: {key}") + return selected + + +def compare_evaluations( + baseline: dict[str, Any], baseline_hash: str, + candidate: dict[str, Any], candidate_hash: str, +) -> dict[str, Any]: + """Reject benchmark changes, regressions, and gains below the fixed threshold.""" + before = _result(baseline, baseline_hash) + after = _result(candidate, candidate_hash) + identity = ("manifestSha256", "featureSchema", "split", "difficulty", "seed", "deterministic", "runtime", "evaluatorSha256") + if any(key not in baseline or key not in candidate or baseline[key] != candidate[key] for key in identity): + raise ValueError("Benchmark identity differs; re-evaluate both checkpoints on the same benchmark") + old = {track["id"]: track for track in before["tracks"]} + new = {track["id"]: track for track in after["tracks"]} + if old.keys() != new.keys(): + raise ValueError("Held-out track sets differ") + target = TARGET_NPS_MAP.get(candidate["difficulty"]) + if target is None: + raise ValueError("Unknown evaluation difficulty") + gains = [] + for identity, current in new.items(): + previous = old[identity] + if current["features"] != previous["features"]: + raise ValueError(f"Held-out audio analysis changed: {identity}") + a, b = previous["metrics"], current["metrics"] + for key in ("handImbalance", "mostRepeatedTransitionFraction", "warnings"): + if _finite(b, key) > _finite(a, key) + 1e-9: + raise ValueError(f"Regression in {key} on {identity}") + if abs(_finite(b, "nps") - target) > abs(_finite(a, "nps") - target) + 1e-9: + raise ValueError(f"Regression in target density on {identity}") + gain = _finite(b, PRIMARY_METRIC) - _finite(a, PRIMARY_METRIC) + if gain < -1e-9: + raise ValueError(f"Regression in onset alignment on {identity}") + gains.append(gain) + mean_gain = sum(gains) / len(gains) + if mean_gain < MIN_PRIMARY_GAIN: + raise ValueError(f"Primary metric must improve by at least {MIN_PRIMARY_GAIN}") + return {"primaryMetric": PRIMARY_METRIC, "meanGain": mean_gain, + "minimumGain": MIN_PRIMARY_GAIN, "tracksCompared": len(gains), + "humanPreferenceEstablished": False} + + +@contextmanager +def _locked(root: Path): + root.mkdir(parents=True, exist_ok=True) + lock = root / ".registry.lock" + try: + descriptor = os.open(lock, os.O_CREAT | os.O_EXCL | os.O_WRONLY) + except FileExistsError as exc: + raise ValueError("Registry is locked by another operation; retry after it finishes") from exc + try: + os.close(descriptor) + yield + finally: + lock.unlink() + + +def _save_registry(root: Path, state: dict[str, Any]) -> None: + temporary = root / "registry.json.tmp" + temporary.write_text(json.dumps(state, indent=2, allow_nan=False) + "\n", encoding="utf-8") + temporary.replace(root / "registry.json") + + +def _store_artifact(source: Path, destination: Path, expected: str) -> None: + destination.parent.mkdir(parents=True, exist_ok=True) + if destination.exists(): + if file_sha256(destination) != expected: + raise ValueError("Stored registry artifact failed its content hash") + return + temporary = destination.with_suffix(destination.suffix + ".tmp") + shutil.copyfile(source, temporary) + if file_sha256(temporary) != expected: + temporary.unlink() + raise ValueError("Artifact changed during registry import") + temporary.replace(destination) + + +def register_checkpoint(root: Path, checkpoint: Path, evaluation: Path, *, initialize: bool = False) -> dict[str, Any]: + """Install a baseline explicitly, or promote a measured improvement atomically.""" + from rl.checkpoints import load_checkpoint + + root, checkpoint, evaluation = Path(root), Path(checkpoint), Path(evaluation) + _, metadata = load_checkpoint(checkpoint) + digest = metadata["checkpointSha256"] + report = _read(evaluation) + report_hash = file_sha256(evaluation) + selected = _result(report, digest) + if selected["checkpoint"].get("trainingSourceSha256") != metadata.get("trainingSourceSha256"): + raise ValueError("Evaluation training lineage does not match checkpoint metadata") + with _locked(root): + state = _read(root / "registry.json") if (root / "registry.json").exists() else {"schemaVersion": 1, "active": None, "models": {}, "history": []} + active = state["active"] + if initialize: + if active is not None: + raise ValueError("Registry already has a baseline; use promote") + decision = {"baselineInitialized": True, "humanPreferenceEstablished": False} + else: + if active is None: + raise ValueError("Initialize an evaluated baseline before promotion") + if active == digest: + raise ValueError("Candidate is already the active checkpoint") + previous = state["models"][active] + baseline_path = root / previous["evaluation"] + if file_sha256(baseline_path) != previous["evaluationSha256"]: + raise ValueError("Stored baseline evaluation changed") + decision = compare_evaluations(_read(baseline_path), active, report, digest) + checkpoint_name = f"checkpoints/{digest}.pt" + evaluation_name = f"evaluations/{report_hash}.json" + _store_artifact(checkpoint, root / checkpoint_name, digest) + _store_artifact(evaluation, root / evaluation_name, report_hash) + state["models"][digest] = {"checkpoint": checkpoint_name, "evaluation": evaluation_name, + "evaluationSha256": report_hash, "metadata": metadata} + state["history"].append({"action": "initialize" if initialize else "promote", "from": active, "to": digest, + "at": datetime.now(timezone.utc).isoformat(), "decision": decision}) + state["active"] = digest + _save_registry(root, state) + return {"active": digest, "previous": active, "checkpoint": str((root / checkpoint_name).resolve()), "decision": decision} + + +def active_checkpoint(root: Path) -> Path: + root = Path(root) + state = _read(root / "registry.json") + digest = state.get("active") + if not digest: + raise ValueError("Registry has no active checkpoint") + if not isinstance(digest, str) or len(digest) != 64 or any(c not in "0123456789abcdef" for c in digest): + raise ValueError("Registry contains an invalid checkpoint identity") + path = root / "checkpoints" / f"{digest}.pt" + if file_sha256(path) != digest: + raise ValueError("Active checkpoint failed its recorded content hash") + return path + + +def registry_status(root: Path) -> dict[str, Any]: + """Compact read-only studio status; does not import PyTorch or expose paths.""" + root = Path(root) + if not (root / "registry.json").is_file(): + return {"status": "empty", "active": None, "modelCount": 0, "automaticTraining": False} + try: + state = _read(root / "registry.json") + active_checkpoint(root) + model = state["models"][state["active"]] + metadata = model.get("metadata", {}) + history = state.get("history", []) + return { + "status": "ready", "active": state["active"], "modelCount": len(state["models"]), + "featureSchema": metadata.get("featureSchema"), + "trainingTrackCount": len(metadata.get("trainingSourceSha256", [])), + "lastAction": history[-1] if history else None, + "automaticTraining": False, "humanPreferenceEstablished": False, + } + except (ValueError, KeyError, TypeError, OSError) as exc: + return {"status": "invalid", "active": None, "automaticTraining": False, + "error": "Registry or stored checkpoint integrity check failed"} + + +def rollback(root: Path) -> dict[str, Any]: + root = Path(root) + with _locked(root): + state = _read(root / "registry.json") + active = state["active"] + previous = next((entry["from"] for entry in reversed(state["history"]) + if entry["to"] == active and entry["from"] is not None), None) + if previous is None: + raise ValueError("No previous active checkpoint exists") + path = root / "checkpoints" / f"{previous}.pt" + if file_sha256(path) != previous: + raise ValueError("Rollback checkpoint failed its recorded content hash") + state["active"] = previous + state["history"].append({"action": "rollback", "from": active, "to": previous, + "at": datetime.now(timezone.utc).isoformat()}) + _save_registry(root, state) + return {"active": previous, "previous": active, "checkpoint": str(path.resolve())} + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--registry", required=True, type=Path) + commands = parser.add_subparsers(dest="command", required=True) + for command in ("initialize", "promote"): + sub = commands.add_parser(command) + sub.add_argument("--checkpoint", type=Path, required=True) + sub.add_argument("--evaluation", type=Path, required=True) + commands.add_parser("rollback") + commands.add_parser("active") + args = parser.parse_args() + try: + if args.command == "active": + result = {"checkpoint": str(active_checkpoint(args.registry).resolve())} + elif args.command == "rollback": + result = rollback(args.registry) + else: + result = register_checkpoint(args.registry, args.checkpoint, args.evaluation, initialize=args.command == "initialize") + except (ValueError, FileNotFoundError) as exc: + parser.error(str(exc)) + print(json.dumps(result, indent=2)) + return 0 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/skills/beat-saber-mapping/scripts/rl/safety.py b/skills/beat-saber-mapping/scripts/rl/safety.py new file mode 100644 index 0000000..3cb5d19 --- /dev/null +++ b/skills/beat-saber-mapping/scripts/rl/safety.py @@ -0,0 +1,86 @@ +"""RL checks in the same constant-BPM coordinates used by the package validator.""" + +from __future__ import annotations + +import math +from collections import Counter +from typing import Any + +import numpy as np + +from kinematics import transition_finding +from safety_contract import MIN_CHAIN_DURATION_BEATS, RECOVERY_BEATS, occupied_until, same_hand_after_hold_too_soon + + +class ValidationClock: + def __init__(self, audio: dict[str, Any], grid: list[float], bpm: float): + self.grid = grid + self.bpm = bpm + times = audio.get("timesSeconds") + if times is None: + self.exported = list(grid) + else: + if len(times) != len(grid) or not np.isfinite(times).all() or np.any(np.diff(times) <= 0): + raise ValueError("RL time positions must match the grid and increase monotonically") + self.exported = [round(float(time) * bpm / 60.0, 8) for time in times] + + def beat(self, beat: float) -> float: + return float(np.interp(beat, self.grid, self.exported)) + + def note(self, note: dict[str, Any] | None) -> dict[str, Any] | None: + return {**note, "b": self.beat(float(note["b"]))} if note is not None else None + + +def transition_is_safe(previous: dict[str, Any], current: dict[str, Any], bpm: float, difficulty: str) -> bool: + if transition_finding(previous, current, bpm, recovery_beats=RECOVERY_BEATS[difficulty]) is not None: + return False + # validate_v3 also enforces this reach cap independently of the swing model. + # Keep validator-based adversarial tests to detect any future contract drift. + gap = float(current["b"]) - float(previous["b"]) + distance = math.hypot(int(current["x"]) - int(previous["x"]), int(current["y"]) - int(previous["y"])) + return not (gap <= .25 and distance > 2.5) + + +def safe_hold_candidates(notes, arcs, chains, audio, grid, bpm, difficulty): + """Keep only decorations compatible with the already selected hand paths. + + The policy currently selects notes, not holds. Decoration must therefore + respect those notes and must never rewrite a valid swing into an unsafe exit. + """ + clock = ValidationClock(audio, grid, bpm) + hands = {color: sorted((clock.note(note) for note in notes if note["c"] == color), key=lambda note: note["b"]) + for color in (0, 1)} + accepted = {"arc": [], "chain": []} + busy = {0: -math.inf, 1: -math.inf} + rejected = Counter() + proposals = [("arc", item) for item in arcs] + [("chain", item) for item in chains] + for kind, item in sorted(proposals, key=lambda proposal: (proposal[1]["b"], proposal[0])): + color = item["c"] + start, tail = clock.beat(item["b"]), clock.beat(item["tb"]) + if item["tb"] > grid[-1] or tail <= start: + rejected["outside_grid"] += 1 + continue + if kind == "chain" and tail - start < MIN_CHAIN_DURATION_BEATS - 1e-8: + rejected["chain_too_short"] += 1 + continue + if start < busy[color]: + rejected["hold_occupancy"] += 1 + continue + hand = hands[color] + inside = [note for note in hand if start + 1e-6 < note["b"] < tail - 1e-6] + if inside or (kind == "chain" and any(abs(note["b"] - tail) <= 1e-6 for note in hand)): + rejected["note_occupancy"] += 1 + continue + next_note = next((note for note in hand if note["b"] > tail + 1e-6), None) + exit_pose = {"b": tail, "c": color, "x": item["tx"], "y": item["ty"], "d": item.get("tc", item["d"])} + if next_note is not None: + gap = next_note["b"] - tail + if same_hand_after_hold_too_soon(gap) or next_note["b"] < occupied_until(tail, RECOVERY_BEATS[difficulty]): + rejected["post_hold_recovery"] += 1 + continue + if not transition_is_safe(exit_pose, next_note, bpm, difficulty): + rejected["tail_exit_motion"] += 1 + continue + accepted[kind].append(item) + busy[color] = occupied_until(tail, RECOVERY_BEATS[difficulty]) + return accepted["arc"], accepted["chain"], dict(rejected) diff --git a/skills/beat-saber-mapping/scripts/rl/train_custom_tracks.py b/skills/beat-saber-mapping/scripts/rl/train_custom_tracks.py index ddab251..6a56d98 100644 --- a/skills/beat-saber-mapping/scripts/rl/train_custom_tracks.py +++ b/skills/beat-saber-mapping/scripts/rl/train_custom_tracks.py @@ -1,162 +1,122 @@ #!/usr/bin/env python3 -"""Train RL Policy on User Tracks: Daft Punk R.A.M and Ninajirachi.""" +"""Train on verified local analyzer artifacts using the inference feature contract.""" from __future__ import annotations import argparse -import io -import sys from pathlib import Path -from typing import Any, Dict, List, Optional, Tuple +from typing import Any -if hasattr(sys.stdout, "reconfigure"): - sys.stdout.reconfigure(encoding="utf-8", errors="replace") -if hasattr(sys.stderr, "reconfigure"): - sys.stderr.reconfigure(encoding="utf-8", errors="replace") - -import numpy as np import torch -try: - from .environment import BeatSaberEnv - from .models import ActorCriticPolicy - from .train_ppo import train_ppo -except ImportError: - from rl.environment import BeatSaberEnv - from rl.models import ActorCriticPolicy - from rl.train_ppo import train_ppo - -from beatforge_core import AudioBuffer, frame_features, load_audio - - -def extract_track_features(audio_path: Path) -> Tuple[Dict[str, Any], List[float], float]: - """Extract audio features, beat grid, and BPM for RL environment training.""" - print(f"Extracting features from {audio_path.name}...", flush=True) - audio = load_audio(audio_path) - features = frame_features(audio.samples) - hop = int(features["hop"][0]) - sr = audio.sample_rate +from rl.checkpoints import load_checkpoint +from rl.environment import BeatSaberEnv +from rl.features import file_sha256, load_analysis_features +from rl.library import load_library +from rl.models import ActorCriticPolicy +from rl.train_ppo import train_ppo - flux = features["flux"] - bpm_est = 120.0 # default fallback - # Compute rough tempo / beat grid - length = len(flux) - grid_len = max(32, int(audio.duration_samples / (sr * 0.5))) - beat_grid = [round(i * 0.25, 4) for i in range(grid_len)] +def extract_track_features(audio_path: Path, analysis_dir: Path | None = None) -> tuple[dict[str, Any], list[float], float]: + """Load existing analysis; raw audio must match its recorded source hash. - # Interpolate flux into onsets - onsets = np.interp(np.linspace(0, len(flux), grid_len), np.arange(len(flux)), flux) - onsets = (onsets - np.min(onsets)) / (np.max(onsets) - np.min(onsets) + 1e-6) - - # Simulated stem profiles based on transient frequency bands - audio_features = { - "onsets": onsets.tolist(), - "flux": onsets.tolist(), - "stems": { - "drums": (onsets * 0.8 + np.random.uniform(0.0, 0.2, grid_len)).tolist(), - "bass": (onsets * 0.6 + np.random.uniform(0.0, 0.4, grid_len)).tolist(), - "vocals": (onsets * 0.5 + np.random.uniform(0.0, 0.3, grid_len)).tolist(), - "guitar": (onsets * 0.4 + np.random.uniform(0.0, 0.2, grid_len)).tolist(), - "piano": (onsets * 0.3 + np.random.uniform(0.0, 0.2, grid_len)).tolist(), - "other": (onsets * 0.3 + np.random.uniform(0.0, 0.2, grid_len)).tolist(), - }, - "sections": ["verse"] * grid_len, - } - - return audio_features, beat_grid, bpm_est + A directory can be supplied directly. For an audio file, the default artifact + directory is its sibling ``.analysis``. Analysis and human timing review + run before training, so no background model downloads or fake clocks occur. + """ + audio_path = Path(audio_path) + directory = analysis_dir or (audio_path if audio_path.is_dir() else audio_path.with_suffix(".analysis")) + bundle = load_analysis_features(directory) + if audio_path.is_file() and file_sha256(audio_path) != bundle.provenance.get("sourceSha256"): + raise ValueError(f"Analysis does not belong to audio file {audio_path.name}") + if audio_path.is_file(): + bundle.provenance["sourceAudioVerified"] = True + return bundle.audio_features, bundle.beat_grid, bundle.bpm def train_on_custom_library( - tracks: List[Path], + tracks: list[Path], timesteps_per_track: int = 16384, epochs: int = 4, lr: float = 2e-4, model_path: Path = Path("data/models/ppo_policy.pt"), -) -> None: + *, + rounds: int = 1, + seed: int = 0, + resume: bool = True, + held_out_hashes: set[str] | None = None, +) -> dict[str, Any]: + if not tracks or rounds <= 0: + raise ValueError("Training requires tracks and a positive number of rounds") + # Validate the entire library before updating any weights. + prepared = [(Path(track), extract_track_features(Path(track))) for track in tracks] + training_hashes: set[str] = set() + for track, (features, _, _) in prepared: + source = features["provenance"].get("sourceSha256") + if not isinstance(source, str) or len(source) != 64: + raise ValueError(f"Training analysis requires the audio source SHA-256: {track}") + training_hashes.add(source) + held_out_hashes = held_out_hashes or set() + torch.manual_seed(seed) policy = ActorCriticPolicy() - if model_path.is_file(): - try: - policy.load_state_dict(torch.load(model_path, map_location="cpu")) - print(f"Resumed existing weights from {model_path}", flush=True) - except Exception as e: - print(f"Could not load checkpoint: {e}", flush=True) - - print(f"\n=======================================================", flush=True) - print(f" Starting Continuous RL Training on {len(tracks)} User Tracks", flush=True) - print(f"=======================================================\n", flush=True) - - for round_idx in range(1, 100): - print(f"\n--- Training Round {round_idx} Across Library ---", flush=True) - for idx, track_path in enumerate(tracks, 1): - if not track_path.is_file(): - continue - print(f"\n[{idx}/{len(tracks)}] Processing: {track_path.name}", flush=True) - try: - audio_feats, beat_grid, bpm = extract_track_features(track_path) - env = BeatSaberEnv( - audio_features=audio_feats, - beat_grid=beat_grid, - bpm=bpm, - difficulty="Expert", - ) - train_ppo( - policy=policy, - env=env, - total_timesteps=timesteps_per_track, - rollout_steps=512, - ppo_epochs=epochs, - lr=lr, - save_path=model_path, - ) - except Exception as e: - print(f"Error training on {track_path.name}: {e}", flush=True) + prior: dict[str, Any] = {} + if resume and model_path.is_file(): + policy, prior = load_checkpoint(model_path) + if not prior.get("trainingSourceSha256"): + raise ValueError("Checkpoint has no training-library provenance. Use --no-resume for a traceable run.") + training_hashes.update(prior["trainingSourceSha256"]) + if training_hashes & held_out_hashes: + raise ValueError("Held-out audio appears in the checkpoint or requested training library") + + runs = [] + for round_index in range(rounds): + for index, (track, (features, grid, bpm)) in enumerate(prepared): + run_seed = seed + round_index * len(prepared) + index + print(f"Round {round_index + 1}/{rounds}, track {index + 1}/{len(prepared)}: {track.name}", flush=True) + env = BeatSaberEnv(features, grid, bpm, difficulty="Expert") + metrics = train_ppo( + policy, env, total_timesteps=timesteps_per_track, + rollout_steps=min(512, timesteps_per_track), ppo_epochs=epochs, + lr=lr, save_path=model_path, seed=run_seed, + checkpoint_metadata={ + "trainingSourceSha256": sorted(training_hashes), + "heldOutSourceSha256": sorted(held_out_hashes), + "librarySeed": seed, "round": round_index + 1, + "resumedCheckpointSha256": prior.get("checkpointSha256"), + }, + ) + runs.append({"sourceSha256": features["provenance"]["sourceSha256"], **metrics}) + return {"seed": seed, "rounds": rounds, "runs": runs, "checkpoint": str(model_path)} def main() -> int: - parser = argparse.ArgumentParser(description="Train RL policy on specific tracks") - parser.add_argument("--ram-dir", type=Path, default=Path(r"C:\Users\user\Downloads\R.A.M")) - parser.add_argument("--ninajirachi-dir", type=Path, default=Path(r"data\downloads\ninajirachi")) - parser.add_argument("--spotify-dir", type=Path, default=Path(r"data\downloads\spotify_july")) + parser = argparse.ArgumentParser(description=__doc__) + source = parser.add_mutually_exclusive_group(required=True) + source.add_argument("--analysis-dir", action="append", type=Path, help="Repeat for verified analysis directories") + source.add_argument("--manifest", type=Path, help="JSON song library with explicit train/validation/test splits") parser.add_argument("--timesteps-per-track", type=int, default=16384) + parser.add_argument("--rounds", type=int, default=1) + parser.add_argument("--epochs", type=int, default=4) + parser.add_argument("--lr", type=float, default=2e-4) + parser.add_argument("--seed", type=int, default=0) + parser.add_argument("--resume", action=argparse.BooleanOptionalAction, default=True) parser.add_argument("--out", type=Path, default=Path("data/models/ppo_policy.pt")) args = parser.parse_args() - - tracks: List[Path] = [] - if args.ram_dir.is_dir(): - for ext in ("*.mp3", "*.ogg", "*.wav", "*.m4a"): - tracks.extend(sorted(args.ram_dir.glob(ext))) - - downloads_root = Path("data/downloads") - if downloads_root.is_dir(): - for ext in ("**/*.mp3", "**/*.ogg", "**/*.wav", "**/*.webm", "**/*.m4a"): - tracks.extend(sorted(downloads_root.glob(ext))) - - # Deduplicate paths - unique_tracks = [] - seen = set() - for p in tracks: - res = p.resolve() - if res not in seen and p.is_file(): - seen.add(res) - unique_tracks.append(p) - tracks = unique_tracks - - print(f"Found {len(tracks)} training tracks:") - for t in tracks: - print(f" - {t.name}") - - if not tracks: - print("No audio tracks found!") - return 1 - - train_on_custom_library( - tracks=tracks, - timesteps_per_track=args.timesteps_per_track, - model_path=args.out, - ) + try: + held_out: set[str] = set() + tracks = args.analysis_dir or [] + if args.manifest: + library = load_library(args.manifest) + tracks = [Path(track["analysisDir"]) for track, _ in library if track["split"] == "train"] + held_out = {bundle.provenance["sourceSha256"] for track, bundle in library if track["split"] != "train"} + train_on_custom_library( + tracks, args.timesteps_per_track, args.epochs, args.lr, args.out, + rounds=args.rounds, seed=args.seed, resume=args.resume, held_out_hashes=held_out, + ) + except (ValueError, FileNotFoundError) as exc: + parser.error(str(exc)) return 0 if __name__ == "__main__": - sys.exit(main()) + raise SystemExit(main()) diff --git a/skills/beat-saber-mapping/scripts/rl/train_ppo.py b/skills/beat-saber-mapping/scripts/rl/train_ppo.py index 480b6f0..d315d4c 100644 --- a/skills/beat-saber-mapping/scripts/rl/train_ppo.py +++ b/skills/beat-saber-mapping/scripts/rl/train_ppo.py @@ -12,6 +12,8 @@ import torch import torch.nn as nn import torch.optim as optim +from rl.checkpoints import load_checkpoint, save_checkpoint +from rl.features import load_analysis_features try: from .environment import BeatSaberEnv, NUM_HAND_ACTIONS @@ -70,12 +72,10 @@ def compute_gae(self, last_value: float, gamma: float = 0.99, gae_lambda: float """Compute Generalized Advantage Estimation (GAE).""" last_gae = 0.0 for t in reversed(range(self.ptr)): - if t == self.ptr - 1: - next_val = last_value - next_done = 0.0 - else: - next_val = self.values[t + 1] - next_done = self.dones[t] + next_val = last_value if t == self.ptr - 1 else self.values[t + 1] + # dones[t] belongs to the transition at t, including the last slot. + # After a terminal step the caller's next observation may be a reset. + next_done = self.dones[t] delta = self.rewards[t] + gamma * next_val * (1.0 - next_done) - self.values[t] last_gae = delta + gamma * gae_lambda * (1.0 - next_done) * last_gae @@ -84,7 +84,8 @@ def compute_gae(self, last_value: float, gamma: float = 0.99, gae_lambda: float self.returns[: self.ptr] = self.advantages[: self.ptr] + self.values[: self.ptr] # Normalize advantages valid_adv = self.advantages[: self.ptr] - self.advantages[: self.ptr] = (valid_adv - valid_adv.mean()) / (valid_adv.std() + 1e-8) + if self.ptr: + self.advantages[: self.ptr] = (valid_adv - valid_adv.mean()) / (valid_adv.std(unbiased=False) + 1e-8) def reset(self) -> None: self.ptr = 0 @@ -105,23 +106,30 @@ def train_ppo( vf_coef: float = 0.5, device: str = "cpu", save_path: Optional[Path] = None, + seed: int = 0, + checkpoint_metadata: Optional[Dict[str, Any]] = None, ) -> Dict[str, float]: + if min(total_timesteps, rollout_steps, ppo_epochs, batch_size) <= 0 or lr <= 0: + raise ValueError("Training steps, epochs, batch size, and learning rate must be positive") + torch.manual_seed(seed) + rng = np.random.default_rng(seed) policy.to(device) optimizer = optim.Adam(policy.parameters(), lr=lr, eps=1e-5) buffer = RolloutBuffer(buffer_size=rollout_steps, obs_dim=env.obs_dim, device=device) - obs, _ = env.reset() + obs, _ = env.reset(seed=seed) timesteps_done = 0 iteration = 0 history: Dict[str, List[float]] = {"ep_return": [], "policy_loss": [], "value_loss": []} + current_ep_reward = 0.0 + completed_returns: List[float] = [] while timesteps_done < total_timesteps: buffer.reset() ep_rewards = [] - current_ep_reward = 0.0 policy.eval() - for step in range(rollout_steps): + for step in range(min(rollout_steps, total_timesteps - timesteps_done)): mask_red, mask_blue = env.action_masks() t_obs = torch.tensor(obs, dtype=torch.float32, device=device).unsqueeze(0) t_mask_r = torch.tensor(mask_red, dtype=torch.bool, device=device).unsqueeze(0) @@ -129,15 +137,28 @@ def train_ppo( with torch.no_grad(): (act_red, act_blue), log_prob, _, val = policy.get_action_and_value( - t_obs, mask_red=t_mask_r, mask_blue=t_mask_b + t_obs, mask_red=t_mask_r, mask_blue=t_mask_b, + blue_mask_fn=lambda selected: torch.as_tensor( + env.blue_action_mask(int(selected.item()), mask_blue), dtype=torch.bool, device=device + ).unsqueeze(0), ) a_r = int(act_red.item()) a_b = int(act_blue.item()) + # Replay must use the same conditional distribution as collection. + mask_blue = env.blue_action_mask(a_r, mask_blue) next_obs, reward, terminated, truncated, info = env.step((a_r, a_b)) current_ep_reward += reward + # A time limit is not an MDP terminal. Bootstrap its final observation, + # then cut the advantage trace before the reset episode. + if truncated and not terminated: + with torch.no_grad(): + final_obs = torch.as_tensor(next_obs, dtype=torch.float32, device=device).unsqueeze(0) + final_value = policy.forward(final_obs)[2].item() + reward += gamma * float(final_value) + buffer.add( obs=obs, act_red=a_r, @@ -155,12 +176,13 @@ def train_ppo( if terminated or truncated: ep_rewards.append(current_ep_reward) + completed_returns.append(current_ep_reward) current_ep_reward = 0.0 obs, _ = env.reset() with torch.no_grad(): t_obs = torch.tensor(obs, dtype=torch.float32, device=device).unsqueeze(0) - _, _, _, last_val = policy.get_action_and_value(t_obs) + _, _, last_val = policy.forward(t_obs) last_value = float(last_val.item()) buffer.compute_gae(last_value=last_value, gamma=gamma, gae_lambda=gae_lambda) @@ -173,7 +195,7 @@ def train_ppo( indices = np.arange(buffer.ptr) for epoch in range(ppo_epochs): - np.random.shuffle(indices) + rng.shuffle(indices) for start in range(0, len(indices), batch_size): end = start + batch_size mb_idx = indices[start:end] @@ -229,11 +251,21 @@ def train_ppo( if save_path is not None: save_path.parent.mkdir(parents=True, exist_ok=True) - torch.save(policy.state_dict(), save_path) + metadata = dict(checkpoint_metadata or {}) + source = env.audio_features.get("provenance", {}).get("sourceSha256") + if source: + metadata["trainingSourceSha256"] = sorted(set(metadata.get("trainingSourceSha256", [])) | {source}) + save_checkpoint(policy, save_path, { + **metadata, "algorithm": "ppo", "seed": seed, + "lastRunTimesteps": timesteps_done, + "features": env.audio_features.get("provenance", {}), + }) print(f"Saved trained PPO policy to {save_path}", flush=True) return { - "final_mean_reward": float(np.mean(ep_rewards) if ep_rewards else current_ep_reward), + "final_mean_reward": float(np.mean(completed_returns) if completed_returns else current_ep_reward), + "timesteps": timesteps_done, + "completed_episodes": len(completed_returns), "policy_loss": avg_p_loss, "value_loss": avg_v_loss, } @@ -244,18 +276,28 @@ def main() -> int: parser.add_argument("--timesteps", type=int, default=1024) parser.add_argument("--rollout-steps", type=int, default=256) parser.add_argument("--epochs", type=int, default=4) - parser.add_argument("--resume", action="store_true", default=True, help="Resume from existing checkpoint if available") + parser.add_argument("--resume", action=argparse.BooleanOptionalAction, default=True) + parser.add_argument("--lr", type=float, default=3e-4) + parser.add_argument("--seed", type=int, default=0) + source = parser.add_mutually_exclusive_group(required=True) + source.add_argument("--analysis-dir", type=Path, help="Verified analyzer artifact directory") + source.add_argument("--synthetic", action="store_true", help="Explicit smoke-test pulse, not real-song training") parser.add_argument("--out", type=Path, default=Path("data/models/ppo_policy.pt")) args = parser.parse_args() - env = BeatSaberEnv(difficulty="Expert") + torch.manual_seed(args.seed) + if args.analysis_dir: + bundle = load_analysis_features(args.analysis_dir) + env = BeatSaberEnv(bundle.audio_features, bundle.beat_grid, bundle.bpm, difficulty="Expert") + else: + env = BeatSaberEnv(difficulty="Expert") policy = ActorCriticPolicy() + prior_metadata = {} if args.resume and args.out.is_file(): - try: - policy.load_state_dict(torch.load(args.out, map_location="cpu")) - print(f"Resumed policy weights from {args.out}", flush=True) - except Exception as e: - print(f"Could not load checkpoint: {e}, starting from scratch", flush=True) + policy, prior_metadata = load_checkpoint(args.out) + if args.analysis_dir and not prior_metadata.get("trainingSourceSha256"): + parser.error("Checkpoint training lineage is unknown. Use --no-resume to start a traceable run.") + print(f"Resumed policy weights from {args.out}", flush=True) train_ppo( policy=policy, @@ -265,6 +307,8 @@ def main() -> int: ppo_epochs=args.epochs, lr=args.lr, save_path=args.out, + seed=args.seed, + checkpoint_metadata=prior_metadata, ) return 0 diff --git a/skills/beat-saber-mapping/scripts/timing_export.py b/skills/beat-saber-mapping/scripts/timing_export.py new file mode 100644 index 0000000..c0c180d --- /dev/null +++ b/skills/beat-saber-mapping/scripts/timing_export.py @@ -0,0 +1,83 @@ +"""Project authored musical beats into the constant BPM clock used by exported maps.""" + +from __future__ import annotations + +from bisect import bisect_right +from copy import deepcopy +import math +from typing import Any + + +class ExportClock: + def __init__(self, analysis: dict[str, Any]): + self.bpm = float(analysis["bpm"]) + self.sample_rate = int(analysis.get("sampleRate", 44100)) + rows = analysis.get("beatGrid", []) + if len(rows) < 2: + raise ValueError("Export timing requires at least two adopted beat/sample rows") + self.beats = [float(row["beat"]) for row in rows] + self.samples = [float(row["sample"]) for row in rows] + if not math.isfinite(self.bpm) or self.bpm <= 0 or self.sample_rate <= 0: + raise ValueError("Invalid export BPM or sample rate") + if not all(math.isfinite(value) for value in self.beats + self.samples): + raise ValueError("Export timing grid contains a non-finite position") + if any(right <= left for values in (self.beats, self.samples) for left, right in zip(values, values[1:])): + raise ValueError("Export timing grid must increase in beat and sample positions") + + def beat(self, authored: float) -> float: + index = min(len(self.beats) - 2, max(0, bisect_right(self.beats, authored) - 1)) + ratio = (authored - self.beats[index]) / (self.beats[index + 1] - self.beats[index]) + sample = self.samples[index] + ratio * (self.samples[index + 1] - self.samples[index]) + # Round to the source sample clock before writing fractional export beats. + return round(round(sample) / self.sample_rate * self.bpm / 60.0, 8) + + +def project_map_timing(beatmap: dict[str, Any], analysis: dict[str, Any]) -> dict[str, Any]: + """Keep absolute and relative generated v3 event times on the analyzed sample clock.""" + clock = ExportClock(analysis) + result = deepcopy(beatmap) + if result.get("customData", {}).get("_beatforgeTiming"): + raise ValueError("Map timing has already been projected") + if result.get("bpmEvents"): + raise ValueError("Authored maps with BPM events require an explicit BPM-event conversion") + for key in ("colorNotes", "bombNotes", "sliders", "burstSliders", "obstacles", "rotationEvents", "basicBeatmapEvents", "colorBoostBeatmapEvents"): + for item in result.get(key, []): + start = float(item["b"]) + item["b"] = clock.beat(start) + if key in {"sliders", "burstSliders"}: + item["tb"] = clock.beat(float(item["tb"])) + elif key == "obstacles": + item["d"] = round(clock.beat(start + float(item["d"])) - item["b"], 8) + if key in {"basicBeatmapEvents", "colorBoostBeatmapEvents"}: + item["b"] = max(0.0, item["b"]) + for key in ("lightColorEventBoxGroups", "lightRotationEventBoxGroups", "lightTranslationEventBoxGroups"): + for group in result.get(key, []): + start = float(group["b"]) + exported_start = clock.beat(start) + group["b"] = max(0.0, exported_start) + for box in group.get("e", []): + # Only event arrays contain relative beat times. The filter's b is a flag. + for array in ("e", "l", "t"): + for event in box.get(array, []): + if "b" in event: + event["b"] = round(clock.beat(start + float(event["b"])) - exported_start, 8) + result["bpmEvents"] = [] + result.setdefault("customData", {})["_beatforgeTiming"] = { + "schemaVersion": 1, "clock": "constant-bpm-sample-projection", + "sampleRate": clock.sample_rate, "bpm": clock.bpm, + } + return result + + +def project_plan_timing(plan: dict[str, Any], analysis: dict[str, Any]) -> dict[str, Any]: + clock = ExportClock(analysis) + result = deepcopy(plan) + for section in result.get("sections", []): + for key in ("startBeat", "endBeat"): + if key in section: + section["authored" + key[0].upper() + key[1:]] = section[key] + section[key] = clock.beat(float(section[key])) + section["startSeconds"] = float(section["startBeat"]) * 60.0 / clock.bpm + section["endSeconds"] = float(section["endBeat"]) * 60.0 / clock.bpm + result["exportClock"] = "constant-bpm-sample-projection" + return result diff --git a/skills/beat-saber-mapping/tests/test_rl_environment.py b/skills/beat-saber-mapping/tests/test_rl_environment.py index 91d5d32..4822f82 100644 --- a/skills/beat-saber-mapping/tests/test_rl_environment.py +++ b/skills/beat-saber-mapping/tests/test_rl_environment.py @@ -179,7 +179,7 @@ def test_ppo_training_loop(): def test_rl_map_generator_v3(): - generator = RLMapGenerator() + generator = RLMapGenerator(policy=ActorCriticPolicy()) bpm = 128.0 grid = [round(i * 0.25, 4) for i in range(32 * 4)] # 32 beats audio = { diff --git a/skills/beat-saber-mapping/tests/test_rl_features.py b/skills/beat-saber-mapping/tests/test_rl_features.py new file mode 100644 index 0000000..b795c91 --- /dev/null +++ b/skills/beat-saber-mapping/tests/test_rl_features.py @@ -0,0 +1,115 @@ +"""Regression checks for real analysis clocks and truthful RL inputs.""" + +import json + +import numpy as np +import pytest + +from rl.features import build_analysis_features, load_analysis_features, STEMS +from rl.library import load_library + + +def documents(): + # Offset is half a second; tempo changes from 120 to 60 at beat 1. + rows = [{"beat": beat, "sample": sample} for beat, sample in + [(0, 22050), (.5, 33075), (1, 44100), (1.5, 66150), (2, 88200)]] + analysis = {"status": "timing_verified", "sampleRate": 44100, "durationSamples": 110250, + "bpm": 120, "sourceSha256": "a" * 64, + "events": [{"sample": 44100, "stemEnergy": {"drums": .8, "bass": .2}}]} + grid = {"sampleRate": 44100, "status": "timing_verified", "beats": rows, "source": "confirmed"} + sections = {"source": "all-in-one", "sections": [ + {"label": "verse", "startSeconds": 0, "endSeconds": 1, "startBeat": 0, "endBeat": 99}, + {"label": "chorus", "startSeconds": 1, "endSeconds": 2.5, "startBeat": 99, "endBeat": 100}, + ]} + frames = {"times": np.arange(11) * .25, "combinedOnset": np.arange(11) / 10, + "flux": np.linspace(1, 0, 11)} + return analysis, grid, sections, frames + + +def write_analysis(path, *, source="a" * 64): + path.mkdir(parents=True, exist_ok=True) + analysis, grid, sections, frames = documents() + analysis["sourceSha256"] = source + for name, doc in [("analysis.json", analysis), ("beat_grid.json", grid), ("sections.json", sections)]: + (path / name).write_text(json.dumps(doc), encoding="utf-8") + np.savez_compressed(path / "audio_features.npz", **frames) + return path + + +def test_offset_and_tempo_changes_use_actual_sample_positions(): + result = build_analysis_features(*documents()) + assert result.beat_grid == [i / 4 for i in range(9)] + assert result.audio_features["samplePositions"] == [22050, 27562, 33075, 38588, 44100, 55125, 66150, 77175, 88200] + assert result.audio_features["onsets"][0] == pytest.approx(.2) + assert result.audio_features["onsets"][-1] == pytest.approx(.8) + assert result.audio_features["stepBpms"][:4] == pytest.approx([120] * 4) + assert result.audio_features["stepBpms"][4:] == pytest.approx([60] * 5) + assert result.provenance["durationSamples"] == 110250 + + +def test_sections_use_seconds_over_stale_constant_tempo_beat_fields(): + result = build_analysis_features(*documents()) + assert result.audio_features["sections"] == ["verse"] * 4 + ["chorus"] * 5 + + +def test_stems_are_real_localized_annotations_and_missing_stems_are_honest(): + result = build_analysis_features(*documents()) + audio = result.audio_features + assert audio["stems"]["drums"] == [0, 0, 0, 0, .8, 0, 0, 0, 0] + assert audio["stemAvailability"]["drums"] is True + assert audio["stemAvailability"]["vocals"] is False + assert audio["stems"]["vocals"] == [0] * 9 + analysis, grid, sections, frames = documents() + analysis["events"] = [] + missing = build_analysis_features(analysis, grid, sections, frames) + assert all(not missing.audio_features["stemAvailability"][stem] for stem in STEMS) + assert all(not any(values) for values in missing.audio_features["stems"].values()) + + +def test_hop_clock_does_not_stretch_frames_to_grid_length(): + analysis, grid, sections, _ = documents() + frames = {"hopSamples": 11025, "combinedOnset": np.arange(11) / 10} + result = build_analysis_features(analysis, grid, sections, frames) + assert result.audio_features["onsets"][0] == pytest.approx(.2) + assert result.audio_features["onsets"][-1] == pytest.approx(.8) + assert result.audio_features["flux"] == [0] * 9 + assert result.provenance["fluxAvailable"] is False + + +@pytest.mark.parametrize("bad", [float("nan"), 33074.5, 22050]) +def test_invalid_sample_grid_fails(bad): + args = list(documents()) + args[1]["beats"][1]["sample"] = bad + with pytest.raises(ValueError, match="strictly increasing"): + build_analysis_features(*args) + + +def test_unconfirmed_timing_requires_explicit_generation_override(): + analysis, grid, sections, frames = documents() + analysis["status"] = grid["status"] = "needs_anchors" + with pytest.raises(ValueError, match="verified"): + build_analysis_features(analysis, grid, sections, frames) + allowed = build_analysis_features(analysis, grid, sections, frames, require_verified=False) + assert allowed.provenance["timingStatus"] == "needs_anchors" + + +def test_artifact_hashes_and_repeatability(tmp_path): + path = write_analysis(tmp_path / "analysis") + first = load_analysis_features(path) + assert first == load_analysis_features(path) + assert len(first.provenance["artifactSha256"]) == 4 + (path / "audio_features.npz").unlink() + with pytest.raises(FileNotFoundError, match="Run analyze_audio"): + load_analysis_features(path) + + +def test_library_rejects_content_overlap_even_if_track_names_differ(tmp_path): + write_analysis(tmp_path / "one") + write_analysis(tmp_path / "two") + manifest = tmp_path / "library.json" + manifest.write_text(json.dumps({"schemaVersion": 1, "tracks": [ + {"id": "first", "analysisDir": "one", "split": "train"}, + {"id": "different-name", "analysisDir": "two", "split": "validation"}, + ]})) + with pytest.raises(ValueError, match="split leakage"): + load_library(manifest) diff --git a/skills/beat-saber-mapping/tests/test_rl_invariants.py b/skills/beat-saber-mapping/tests/test_rl_invariants.py new file mode 100644 index 0000000..2ef21c1 --- /dev/null +++ b/skills/beat-saber-mapping/tests/test_rl_invariants.py @@ -0,0 +1,113 @@ +"""Independent serialization, episode-boundary, and reproducibility regressions.""" + +from collections import Counter + +import numpy as np +import pytest + +torch = pytest.importorskip("torch") + +from beatforge_core import ValidationReport +from rl.checkpoints import save_checkpoint +from rl.environment import BeatSaberEnv +from rl.models import ActorCriticPolicy +from rl.policy_generator import RLMapGenerator +from rl.safety import safe_hold_candidates +from rl.train_ppo import RolloutBuffer, train_ppo +from timing_export import project_map_timing +from validate_map import validate_v3 + + +def fixture_audio(length=96, bpm=160, variable=False): + grid = [index / 4 for index in range(length)] + tempos = [90 if index < length // 2 else 220 for index in range(length)] if variable else [bpm] * length + samples = [11025] + for index in range(length - 1): + samples.append(samples[-1] + round(44100 * 60 / tempos[index] / 4)) + audio = { + "onsets": [.95] * length, "flux": [.8] * length, "sustainBeats": [2.] * length, + "sections": ["chorus"] * length, + "stems": {name: [.9] * length for name in ("drums", "bass", "guitar", "piano", "vocals", "other")}, + "timesSeconds": [sample / 44100 for sample in samples], "stepBpms": tempos, + } + analysis = {"bpm": bpm, "sampleRate": 44100, + "beatGrid": [{"beat": beat, "sample": sample} for beat, sample in zip(grid, samples)]} + return audio, grid, analysis + + +@pytest.mark.parametrize("difficulty", ["Easy", "Hard", "ExpertPlus"]) +@pytest.mark.parametrize("bpm,variable", [(90, False), (220, False), (120, True)]) +@pytest.mark.parametrize("seed", [0, 3]) +def test_dense_adversarial_rl_output_passes_independent_exported_validator(difficulty, bpm, variable, seed): + torch.manual_seed(seed) + audio, grid, analysis = fixture_audio(bpm=bpm, variable=variable) + chart = RLMapGenerator(policy=ActorCriticPolicy(hidden_dim=16)).generate_difficulty( + audio, grid, bpm, difficulty=difficulty, deterministic=False, seed=seed, + ) + report = ValidationReport() + validate_v3(project_map_timing(chart, analysis), "fixture.dat", report, bpm=bpm, difficulty=difficulty) + assert len(chart["colorNotes"]) > len(grid) // 8, "Passing by emitting an empty or nearly empty chart is not acceptable" + assert not report.errors, dict(Counter(issue.code for issue in report.errors)) + + +@pytest.mark.parametrize("deterministic", [True, False]) +def test_checkpoint_generation_is_reproducible_and_preserves_caller_rng(tmp_path, deterministic): + torch.manual_seed(17) + checkpoint = tmp_path / "policy.pt" + save_checkpoint(ActorCriticPolicy(hidden_dim=16), checkpoint, {"trainingSourceSha256": ["a" * 64]}) + audio, grid, _ = fixture_audio(length=40) + state = torch.random.get_rng_state().clone() + first = RLMapGenerator(model_path=checkpoint).generate_difficulty(audio, grid, 160, deterministic=deterministic, seed=42) + assert torch.equal(state, torch.random.get_rng_state()) + torch.rand(13) # Unrelated application use must not change the map's random stream. + second = RLMapGenerator(model_path=checkpoint).generate_difficulty(audio, grid, 160, deterministic=deterministic, seed=42) + assert first == second + if not deterministic: + other = RLMapGenerator(model_path=checkpoint).generate_difficulty(audio, grid, 160, deterministic=False, seed=43) + assert other["colorNotes"] != first["colorNotes"] + + +def test_safe_holds_survive_without_erasing_notes_or_overlapping_ownership(): + notes = [{"b": beat, "c": 0, "x": 1, "y": 0, "d": direction} for beat, direction in [(0, 1), (1, 0), (2, 1), (4, 0)]] + arc = {"b": 0, "c": 0, "x": 1, "y": 0, "d": 1, "tb": 1, "tx": 1, "ty": 0, "tc": 0, "mu": 1, "tmu": 1, "m": 0} + chain = {"b": 2, "c": 0, "x": 1, "y": 0, "d": 1, "tb": 2.25, "tx": 1, "ty": 0, "sc": 5, "s": 1} + conflicting = {**chain, "b": 0, "tb": .25} + grid = [index / 4 for index in range(17)] + arcs, chains, rejected = safe_hold_candidates(notes, [arc], [chain, conflicting], {}, grid, 120, "Easy") + assert arcs == [arc] and chains == [chain] + assert rejected["hold_occupancy"] == 1 + report = ValidationReport() + validate_v3({"version": "3.3.0", "colorNotes": notes, "sliders": arcs, "burstSliders": chains}, + "fixture.dat", report, bpm=120, difficulty="Easy") + assert not report.errors + assert len(notes) == 4 + + +@pytest.mark.parametrize("terminal,expected", [(True, 2.), (False, 8.3)]) +def test_actual_ppo_rollout_distinguishes_terminal_and_time_limit(monkeypatch, terminal, expected): + class BoundaryEnv(BeatSaberEnv): + def reset(self, **kwargs): + super().reset(**kwargs) + return np.zeros(self.obs_dim, dtype=np.float32), {} + + def step(self, action): + return np.ones(self.obs_dim, dtype=np.float32), 2., terminal, not terminal, {} + + policy = ActorCriticPolicy(hidden_dim=16) + # Final observation has value 7; reset observation has deliberately huge value + # 123. Neither a true terminal nor the new episode may bootstrap from 123. + monkeypatch.setattr(policy, "forward", lambda obs: (None, None, torch.where(obs[:, 0] > 0, 7., 123.))) + recorded = {} + original = RolloutBuffer.compute_gae + + def inspect(self, *args, **kwargs): + original(self, *args, **kwargs) + recorded["return"] = self.returns[0].item() + recorded["reward"] = self.rewards[0].item() + recorded["done"] = self.dones[0].item() + + monkeypatch.setattr(RolloutBuffer, "compute_gae", inspect) + result = train_ppo(policy, BoundaryEnv(beat_grid=[0, .25]), total_timesteps=1, + rollout_steps=1, ppo_epochs=1, batch_size=1, gamma=.9, seed=4) + assert recorded == pytest.approx({"return": expected, "reward": expected, "done": 1.}) + assert result["final_mean_reward"] == 2., "Reported episode reward must exclude the bootstrap value" diff --git a/skills/beat-saber-mapping/tests/test_rl_registry.py b/skills/beat-saber-mapping/tests/test_rl_registry.py new file mode 100644 index 0000000..6171bae --- /dev/null +++ b/skills/beat-saber-mapping/tests/test_rl_registry.py @@ -0,0 +1,122 @@ +"""Promotion requires the same benchmark, measured progress, and no regressions.""" + +import copy +import json + +import pytest + +from rl.features import FEATURE_SCHEMA +from rl.registry import active_checkpoint, compare_evaluations, register_checkpoint, rollback, registry_status + + +def evaluation(checkpoint_hash, onset=.5): + return { + "schemaVersion": 1, "kind": "offline-held-out-checkpoint-evaluation", + "featureSchema": FEATURE_SCHEMA, "heldOutVerifiedAgainstRecordedTraining": True, + "split": "validation", "difficulty": "Expert", "seed": 0, "deterministic": True, + "manifestSha256": "d" * 64, "runtime": {"torch": "test", "numpy": "test"}, + "evaluatorSha256": {"evaluate.py": "e" * 64}, + "results": [{"checkpoint": {"checkpointSha256": checkpoint_hash, "trainingSourceSha256": ["a" * 64]}, + "tracks": [{"id": "held-out", "features": { + "sourceSha256": "b" * 64, "timingStatus": "timing_verified", + "artifactSha256": {"beat_grid.json": "c" * 64}, + }, "metrics": {"hardErrors": 0, "notes": 100, "nps": 4.25, + "handImbalance": .1, "mostRepeatedTransitionFraction": .1, + "warnings": 0, "meanOnsetStrengthAtNotes": onset}}]}], + } + + +def test_promotion_requires_primary_gain_and_preserves_other_metrics(): + result = compare_evaluations(evaluation("before"), "before", evaluation("after", .6), "after") + assert result["meanGain"] == pytest.approx(.1) + assert result["humanPreferenceEstablished"] is False + + +@pytest.mark.parametrize("key,value", [ + ("hardErrors", 1), ("notes", 0), ("handImbalance", .2), + ("mostRepeatedTransitionFraction", .3), ("warnings", 1), ("nps", 7), + ("meanOnsetStrengthAtNotes", .5), ("meanOnsetStrengthAtNotes", float("nan")), +]) +def test_regression_or_no_improvement_cannot_promote(key, value): + candidate = evaluation("after", .7) + candidate["results"][0]["tracks"][0]["metrics"][key] = value + with pytest.raises(ValueError): + compare_evaluations(evaluation("before"), "before", candidate, "after") + + +@pytest.mark.parametrize("field,value", [("manifestSha256", "different"), ("seed", 4), + ("difficulty", "Hard"), ("evaluatorSha256", {})]) +def test_changed_benchmark_cannot_promote(field, value): + candidate = evaluation("after", .7) + candidate[field] = value + with pytest.raises(ValueError, match="Benchmark identity"): + compare_evaluations(evaluation("before"), "before", candidate, "after") + + +def test_one_improved_track_cannot_hide_a_regression_elsewhere(): + baseline = evaluation("before") + second = copy.deepcopy(baseline["results"][0]["tracks"][0]) + second["id"] = "second" + second["features"]["sourceSha256"] = "f" * 64 + baseline["results"][0]["tracks"].append(second) + candidate = copy.deepcopy(baseline) + candidate["results"][0]["checkpoint"]["checkpointSha256"] = "after" + candidate["results"][0]["tracks"][0]["metrics"]["meanOnsetStrengthAtNotes"] = 1 + candidate["results"][0]["tracks"][1]["metrics"]["meanOnsetStrengthAtNotes"] = .4 + with pytest.raises(ValueError, match="Regression in onset alignment"): + compare_evaluations(baseline, "before", candidate, "after") + + +def test_registry_promotion_and_rollback_preserve_checkpoint_files(tmp_path): + torch = pytest.importorskip("torch") + from rl.checkpoints import load_checkpoint, save_checkpoint + from rl.models import ActorCriticPolicy + paths = [] + for seed in (1, 2, 3): + torch.manual_seed(seed) + path = tmp_path / f"policy-{seed}.pt" + save_checkpoint(ActorCriticPolicy(hidden_dim=16), path, {"trainingSourceSha256": ["a" * 64]}) + _, metadata = load_checkpoint(path) + report_path = tmp_path / f"report-{seed}.json" + report_path.write_text(json.dumps(evaluation(metadata["checkpointSha256"], .4 + seed / 10))) + paths.append((path, report_path)) + registry = tmp_path / "registry" + baseline = register_checkpoint(registry, *paths[0], initialize=True) + previous_bytes = active_checkpoint(registry).read_bytes() + promoted = register_checkpoint(registry, *paths[1]) + assert promoted["previous"] == baseline["active"] + assert active_checkpoint(registry).read_bytes() == paths[1][0].read_bytes() + status = registry_status(registry) + assert status["status"] == "ready" + assert status["modelCount"] == 2 + assert status["automaticTraining"] is False + assert (registry / "checkpoints" / f"{baseline['active']}.pt").read_bytes() == previous_bytes + reverted = rollback(registry) + assert reverted["active"] == baseline["active"] + assert active_checkpoint(registry).read_bytes() == previous_bytes + assert len(list((registry / "checkpoints").glob("*.pt"))) == 2 + # A rejected promotion must not change active registry state. + bad = evaluation(load_checkpoint(paths[2][0])[1]["checkpointSha256"], .9) + bad["results"][0]["tracks"][0]["metrics"]["hardErrors"] = 1 + paths[2][1].write_text(json.dumps(bad)) + state_before = (registry / "registry.json").read_bytes() + with pytest.raises(ValueError, match="hard validation"): + register_checkpoint(registry, *paths[2]) + assert (registry / "registry.json").read_bytes() == state_before + + +def test_registry_rejects_tampered_active_artifact(tmp_path): + registry = tmp_path / "registry" + (registry / "checkpoints").mkdir(parents=True) + digest = "a" * 64 + (registry / "registry.json").write_text(json.dumps({"active": digest})) + (registry / "checkpoints" / f"{digest}.pt").write_bytes(b"wrong content") + with pytest.raises(ValueError, match="content hash"): + active_checkpoint(registry) + assert registry_status(registry)["status"] == "invalid" + + +def test_uninitialized_registry_has_honest_status(tmp_path): + assert registry_status(tmp_path / "missing") == { + "status": "empty", "active": None, "modelCount": 0, "automaticTraining": False, + } diff --git a/skills/beat-saber-mapping/tests/test_rl_training_correctness.py b/skills/beat-saber-mapping/tests/test_rl_training_correctness.py new file mode 100644 index 0000000..df361ee --- /dev/null +++ b/skills/beat-saber-mapping/tests/test_rl_training_correctness.py @@ -0,0 +1,167 @@ +"""Small deterministic RL regressions; no downloads or long training run.""" + +import json + +import numpy as np +import pytest + +torch = pytest.importorskip("torch") + +from rl.checkpoints import load_checkpoint, save_checkpoint +from rl.environment import BeatSaberEnv, decode_hand_action +from rl.evaluate import chart_metrics, evaluate_checkpoints +from rl.features import load_analysis_features +from rl.models import ActorCriticPolicy +from rl.policy_generator import RLMapGenerator +from rl.train_custom_tracks import extract_track_features, train_on_custom_library +from rl.train_ppo import RolloutBuffer, train_ppo +from test_rl_features import write_analysis +from kinematics import cross_hand_finding, vision_blocking_double + + +def test_terminal_last_slot_does_not_bootstrap_reset_episode_value(): + buffer = RolloutBuffer(1, 69) + buffer.ptr = 1 + buffer.rewards[0] = 3 + buffer.values[0] = 2 + buffer.dones[0] = 1 + buffer.compute_gae(last_value=100, gamma=.9) + assert buffer.returns[0].item() == pytest.approx(3) + assert torch.isfinite(buffer.advantages).all() + + +def test_gae_cuts_internal_episode_boundary_but_bootstraps_nonterminal_tail(): + buffer = RolloutBuffer(3, 69) + buffer.ptr = 3 + buffer.rewards[:] = torch.tensor([1, 2, 5]) + buffer.values[:] = torch.tensor([0, 0, 0]) + buffer.dones[:] = torch.tensor([0, 1, 0]) + buffer.compute_gae(last_value=10, gamma=.5, gae_lambda=1) + assert buffer.returns.tolist() == pytest.approx([2, 2, 10]) + + +def test_inference_requires_checkpoint_and_rejects_corrupt_weights(tmp_path): + with pytest.raises(ValueError, match="explicit trained checkpoint"): + RLMapGenerator() + with pytest.raises(FileNotFoundError): + RLMapGenerator(model_path=tmp_path / "missing.pt") + path = tmp_path / "broken.pt" + path.write_bytes(b"not a checkpoint") + with pytest.raises(ValueError, match="Invalid RL checkpoint"): + RLMapGenerator(model_path=path) + + +def test_checkpoint_hash_and_feature_provenance(tmp_path): + policy = ActorCriticPolicy(hidden_dim=32) + path = tmp_path / "policy.pt" + save_checkpoint(policy, path, {"seed": 4, "trainingSourceSha256": ["a" * 64]}) + loaded, metadata = load_checkpoint(path) + assert metadata["seed"] == 4 + assert len(metadata["checkpointSha256"]) == 64 + assert all(torch.equal(tensor, loaded.state_dict()[key]) for key, tensor in policy.state_dict().items()) + bad = policy.state_dict() + bad["encoder.0.weight"][0, 0] = float("nan") + torch.save(bad, path) + with pytest.raises(ValueError, match="non-finite"): + load_checkpoint(path) + + +def test_training_and_inference_load_identical_features(tmp_path): + path = write_analysis(tmp_path / "analysis") + audio, grid, bpm = extract_track_features(path) + bundle = load_analysis_features(path) + assert audio == bundle.audio_features + assert grid == bundle.beat_grid + assert bpm == bundle.bpm + assert audio["sections"][-1] == "chorus" + + +def test_short_ppo_run_stops_at_requested_steps_and_repeats_with_seed(): + states = [] + for _ in range(2): + torch.manual_seed(23) + policy = ActorCriticPolicy(hidden_dim=16) + env = BeatSaberEnv(beat_grid=[0, .25]) + metrics = train_ppo(policy, env, total_timesteps=3, rollout_steps=2, ppo_epochs=1, batch_size=2, seed=23) + assert metrics["timesteps"] == 3 + assert metrics["completed_episodes"] == 1 + assert np.isfinite(metrics["policy_loss"]) + states.append({key: value.clone() for key, value in policy.state_dict().items()}) + assert all(torch.equal(states[0][key], states[1][key]) for key in states[0]) + + +def test_environment_seed_does_not_mutate_numpy_global_rng(): + np.random.seed(321) + expected = np.random.random() + np.random.seed(321) + env = BeatSaberEnv(beat_grid=[0, .25]) + env.reset(seed=77) + assert np.random.random() == expected + assert not any(env.audio_features["stemAvailability"].values()) + + +def test_blue_mask_rejects_every_unsafe_simultaneous_pair(): + env = BeatSaberEnv(beat_grid=[0, .25]) + for red_action in range(1, env.action_dim): + red_pose = decode_hand_action(red_action) + red = {"b": 0, "c": 0, "x": red_pose[0], "y": red_pose[1], "d": red_pose[2]} + mask = env.blue_action_mask(red_action) + assert mask[0] + for blue_action in np.flatnonzero(mask[1:]) + 1: + x, y, direction = decode_hand_action(int(blue_action)) + blue = {"b": 0, "c": 1, "x": x, "y": y, "d": direction} + assert cross_hand_finding(red, blue) is None + assert not vision_blocking_double(red, blue) + + +def test_conditional_sampling_and_ppo_replay_have_identical_log_probabilities(): + torch.manual_seed(42) + env = BeatSaberEnv(beat_grid=[0, .25]) + policy = ActorCriticPolicy(hidden_dim=16) + obs = torch.as_tensor(env.reset()[0]).unsqueeze(0) + mask_red, mask_blue = env.action_masks() + conditional = lambda chosen: torch.as_tensor(env.blue_action_mask(int(chosen.item()), mask_blue)).unsqueeze(0) + action, sampled_log_prob, _, _ = policy.get_action_and_value( + obs, mask_red=torch.as_tensor(mask_red).unsqueeze(0), + mask_blue=torch.as_tensor(mask_blue).unsqueeze(0), blue_mask_fn=conditional, + ) + effective_blue = conditional(action[0]) + assert effective_blue[0, int(action[1].item())] + _, replay_log_prob, _, _ = policy.get_action_and_value( + obs, action=action, mask_red=torch.as_tensor(mask_red).unsqueeze(0), mask_blue=effective_blue, + ) + assert sampled_log_prob.item() == pytest.approx(replay_log_prob.item()) + + +def test_benchmark_uses_held_out_hashes_and_reports_real_validation(tmp_path): + write_analysis(tmp_path / "train", source="a" * 64) + write_analysis(tmp_path / "held-out", source="b" * 64) + manifest = tmp_path / "library.json" + manifest.write_text(json.dumps({"schemaVersion": 1, "tracks": [ + {"id": "train", "analysisDir": "train", "split": "train"}, + {"id": "held-out", "analysisDir": "held-out", "split": "validation"}, + ]})) + policy = ActorCriticPolicy(hidden_dim=16) + for parameter in policy.parameters(): + parameter.data.zero_() # Deterministic idle policy, only for the fixture. + path = tmp_path / "policy.pt" + save_checkpoint(policy, path, {"trainingSourceSha256": ["a" * 64]}) + result = evaluate_checkpoints(manifest, [path], seed=3) + assert result["heldOutVerifiedAgainstRecordedTraining"] + assert result["results"][0]["tracks"][0]["id"] == "held-out" + assert result["results"][0]["tracks"][0]["metrics"]["notes"] == 0 + assert result["results"][0]["tracks"][0]["metrics"]["headsetPlaytest"] == "not_performed" + save_checkpoint(policy, path, {"trainingSourceSha256": ["b" * 64]}) + with pytest.raises(ValueError, match="used to train"): + evaluate_checkpoints(manifest, [path]) + + +def test_structural_benchmark_detects_colliding_notes(tmp_path): + bundle = load_analysis_features(write_analysis(tmp_path / "analysis")) + chart = {"version": "3.3.0", "colorNotes": [ + {"b": 1, "c": 0, "x": 1, "y": 0, "d": 1}, + {"b": 1, "c": 1, "x": 1, "y": 0, "d": 1}, + ]} + result = chart_metrics(chart, bundle, "Expert") + assert result["hardErrors"] > 0 + assert result["nps"] == pytest.approx(.8) diff --git a/src/beatforge/api.py b/src/beatforge/api.py index ab97657..0298ca8 100644 --- a/src/beatforge/api.py +++ b/src/beatforge/api.py @@ -3,7 +3,10 @@ from __future__ import annotations import json +import math import shutil +import sys +import threading import time import traceback import uuid @@ -17,7 +20,14 @@ from pydantic import BaseModel, Field from beatforge.install import find_custom_levels, install_generated_map_elevated, install_map, launch_beat_saber -from beatforge.premium import ROOT, SKILL, package_map, run_premium_pipeline, summarize_map +from beatforge.premium import ROOT, SKILL, PipelineCancelled, package_map, run_premium_pipeline, summarize_map +from beatforge.mapping_plan import normalize_mapping_plan + +# The RL registry is a portable skill module rather than an installed package. +# Make its import deterministic for a first request made before /api/learning. +_SKILL_SCRIPTS = SKILL / "scripts" +if str(_SKILL_SCRIPTS) not in sys.path: + sys.path.insert(0, str(_SKILL_SCRIPTS)) from beatforge.release_route import ai_release_route_enabled from beatforge.providers import ( PROVIDERS, @@ -39,7 +49,8 @@ IMPORTS_DIR = ROOT / "data" / "imports" SETTINGS_FILE = ROOT / "data" / "provider-settings.json" METADATA_DIR = ROOT / "data" / "metadata" -JOB_TTL_SECONDS = 24 * 3600 +ACTIVE_STATES = {"queued", "running", "validating", "cancelling"} +STATUS_LOCK = threading.RLock() STUDIO_STATES = ( "needs_anchors", "needs_palette", @@ -51,6 +62,8 @@ "release_candidate", "invalid", "error", + "interrupted", + "cancelled", ) TERMINAL_STATES = {state for state in STUDIO_STATES if state != "validating"} STUDIO_DIFFICULTIES = ("Easy", "Normal", "Hard", "Expert", "ExpertPlus") @@ -207,21 +220,26 @@ def _installed_playtest_maps() -> list[dict[str, Any]]: return maps -def _cleanup_old_jobs(max_age: float = JOB_TTL_SECONDS) -> int: +def _reconcile_interrupted_jobs() -> int: + """Keep saved work and make interrupted process-local workers retryable.""" if not JOBS_DIR.is_dir(): return 0 - now = time.time() - removed = 0 + recovered = 0 for directory in JOBS_DIR.iterdir(): if not directory.is_dir(): continue try: - if now - directory.stat().st_mtime > max_age: - shutil.rmtree(directory, ignore_errors=True) - removed += 1 - except OSError: + status = _read_status(directory.name) + if status.get("status") not in ACTIVE_STATES: + continue + cancelled = (directory / "cancel.requested").is_file() + status.update(status="cancelled" if cancelled else "interrupted", localStatus=None) + status = _append_stage(status, status["status"], "Studio restarted before this run finished. Retry uses the saved audio, anchors, palette, and mapping settings.") + _write_status(directory.name, status) + recovered += 1 + except (OSError, ValueError, HTTPException): continue - return removed + return recovered def _metadata_manifest_path(metadata_id: str) -> Path: @@ -265,7 +283,7 @@ def _metadata_cover_path(metadata_id: str | None) -> Path | None: @app.on_event("startup") def _on_startup() -> None: - _cleanup_old_jobs() + _reconcile_interrupted_jobs() def _job_dir(job_id: str) -> Path: @@ -284,9 +302,48 @@ def _read_status(job_id: str) -> dict[str, Any]: def _write_status(job_id: str, data: dict[str, Any]) -> None: directory = _job_dir(job_id) directory.mkdir(parents=True, exist_ok=True) - temporary = directory / "status.json.tmp" - temporary.write_text(json.dumps(data, indent=2), encoding="utf-8") - temporary.replace(directory / "status.json") + with STATUS_LOCK: + timestamp = time.time() + data = {**data, "id": job_id, "createdAt": data.get("createdAt", data.get("startedAt", timestamp)), "updatedAt": timestamp} + _write_json(directory / "status.json", data) + + +def _write_json(path: Path, data: Any) -> None: + temporary = path.with_name(f"{path.name}.{uuid.uuid4().hex}.tmp") + try: + temporary.write_text(json.dumps(data, indent=2), encoding="utf-8") + temporary.replace(path) + finally: + temporary.unlink(missing_ok=True) + + +def _cancel_requested(job_id: str) -> bool: + return (_job_dir(job_id) / "cancel.requested").is_file() + + +def _qa_report(map_dir: Path) -> dict[str, Any]: + """A missing or malformed report cannot establish that hard gates passed.""" + path = map_dir / "_beatforge" / "qa_report.json" + try: + report = json.loads(path.read_text(encoding="utf-8")) + if not isinstance(report, dict) or not isinstance(report.get("errors"), list): + raise ValueError("QA report must contain an errors list") + if not isinstance(report.get("warnings", []), list): + raise ValueError("QA report warnings must be a list") + if report.get("status") not in {"playtest_candidate", "release_candidate"} and not report["errors"]: + raise ValueError("QA report has not passed validation") + return report + except (OSError, ValueError) as error: + return {"status": "invalid", "errors": [f"QA report unavailable or invalid: {error}"], "warnings": []} + + +def _public_job(data: dict[str, Any]) -> dict[str, Any]: + result = {key: value for key, value in data.items() if key != "trace"} + job_id = str(result["id"]) + active = result.get("status") in ACTIVE_STATES + result["canCancel"] = active and result.get("status") != "cancelling" + result["canRetry"] = not active and not result.get("revisionOf") and any(_job_dir(job_id).glob("input.*")) + return result def _audio_path(job_id: str) -> Path: @@ -303,6 +360,8 @@ def _append_stage(data: dict[str, Any], stage: str, detail: str) -> dict[str, An def _install_pack_to_custom_levels(job_id: str, data: dict[str, Any]) -> Path: + if data.get("localStatus") not in {"playtest_candidate", "unconfirmed_pack"} or _qa_report(_job_dir(job_id) / "map")["errors"]: + raise ValueError("The map must pass its local QA report before installation") zip_path = _job_dir(job_id) / "map.zip" if not zip_path.is_file(): raise FileNotFoundError("Generated map zip is missing") @@ -327,7 +386,7 @@ def progress(stage: str, detail: str, percent: float | None = None, log: bool = current = {**current, "stage": stage, "detail": detail} current.update( { - "status": "validating" if stage == "validating" else "running", + "status": "cancelling" if _cancel_requested(job_id) else "validating" if stage == "validating" else "running", "elapsed": round(time.time() - start, 2), "startedAt": current.get("startedAt") or start, } @@ -337,6 +396,8 @@ def progress(stage: str, detail: str, percent: float | None = None, log: bool = _write_status(job_id, current) try: + if _cancel_requested(job_id): + raise PipelineCancelled("Generation cancelled") job_dir = _job_dir(job_id) map_dir = job_dir / "map" anchors = job_dir / "anchors.json" @@ -353,17 +414,21 @@ def progress(stage: str, detail: str, percent: float | None = None, log: bool = palette=palette if palette.is_file() else None, cover=metadata_cover, progress=progress, - allow_unconfirmed=True, + allow_unconfirmed=bool(initial.get("continueUnconfirmed", False)), difficulties=list(initial.get("difficulties") or STUDIO_DIFFICULTIES), + mapping_plan=initial.get("mappingPlan"), + engine=str(initial.get("engine") or "premium"), + rl_model=Path(initial["rlModel"]) if initial.get("rlModel") else None, + cancel_requested=lambda: _cancel_requested(job_id), ) + if _cancel_requested(job_id): + raise PipelineCancelled("Generation cancelled") status = result["status"] map_ready = (map_dir / "Info.dat").is_file() if map_ready and status in {"playtest_candidate", "invalid"}: progress("validating", "checking schema, timing, same-color flow, collisions, hazards, and packaging") - package_map(map_dir, job_dir / "map.zip") summary = summarize_map(map_dir) - qa_path = map_dir / "_beatforge" / "qa_report.json" - qa = json.loads(qa_path.read_text(encoding="utf-8")) if qa_path.is_file() else {"status": "invalid"} + qa = _qa_report(map_dir) qa_failed = bool(qa.get("errors")) analysis_path = map_dir / "_beatforge" / "analysis.json" timing_verified = False @@ -397,6 +462,13 @@ def progress(stage: str, detail: str, percent: float | None = None, log: bool = "timingVerified": timing_verified, } ) + if qa_failed: + current["error"] = "Local validation failed. Open the QA report before retrying." + _write_status(job_id, current) + return + if _cancel_requested(job_id): + raise PipelineCancelled("Generation cancelled") + package_map(map_dir, job_dir / "map.zip") try: destination = _install_pack_to_custom_levels(job_id, current) except Exception as error: @@ -422,8 +494,12 @@ def progress(stage: str, detail: str, percent: float | None = None, log: bool = if status in {"error", "invalid", "corpus_incomplete"}: current["error"] = (result.get("stderr") or result.get("stdout") or "pipeline failed")[-4000:] _write_status(job_id, current) + except PipelineCancelled: + current = _append_stage(_read_status(job_id), "cancelled", "Generation stopped. Retry keeps the saved audio and mapping settings.") + _write_status(job_id, {**current, "status": "cancelled", "localStatus": None, "elapsed": round(time.time() - start, 2)}) except Exception as error: - _write_status(job_id, {**initial, "status": "error", "error": str(error), "trace": traceback.format_exc(), "elapsed": round(time.time() - start, 2)}) + current = _read_status(job_id) + _write_status(job_id, {**current, "status": "error", "localStatus": None, "error": str(error), "trace": traceback.format_exc(), "elapsed": round(time.time() - start, 2)}) def _settings() -> dict[str, Any]: @@ -460,11 +536,13 @@ def _review_passes(job_dir: Path) -> tuple[bool, list[dict[str, Any]]]: def _playtest_gate(job_dir: Path, required: list[str] | None = None) -> dict[str, Any]: path = job_dir / "playtests.json" evidence = json.loads(path.read_text(encoding="utf-8")) if path.is_file() else [] - passing = [item for item in evidence if item.get("passed")] + latest = {(item.get("difficulty"), item.get("speed"), item.get("tester")): item for item in evidence} + failures = {(item.get("difficulty"), item.get("speed")) for item in latest.values() if not item.get("passed")} + passing = [item for item in latest.values() if item.get("passed") and (item.get("difficulty"), item.get("speed")) not in failures] full = {item.get("difficulty") for item in passing if item.get("speed") == "full"} slow = {item.get("difficulty") for item in passing if item.get("speed") == "slow"} fresh = [item for item in passing if item.get("freshSightRead")] - primary_testers = {item.get("tester") for item in passing if not item.get("freshSightRead")} + primary_testers = {item.get("tester") for item in evidence if not item.get("freshSightRead")} required_difficulties = set(required or STUDIO_DIFFICULTIES) expert_slow = {name for name in ("Expert", "ExpertPlus") if name in required_difficulties} return { @@ -472,6 +550,7 @@ def _playtest_gate(job_dir: Path, required: list[str] | None = None) -> dict[str "expertSlow": expert_slow.issubset(slow), "separateFreshSightRead": any(item.get("tester") not in primary_testers for item in fresh), "evidenceCount": len(evidence), + "unresolvedFailures": len(failures), } @@ -493,11 +572,16 @@ def _sync_playtest_provenance(job_dir: Path, playtests: dict[str, Any]) -> dict[ gate["fullSpeedVrPlaytest"] = bool(playtests.get("allFiveFullSpeed")) gate["slowVrPlaytest"] = bool(playtests.get("expertSlow")) gate["freshSightRead"] = bool(playtests.get("separateFreshSightRead")) - path.write_text(json.dumps(payload, indent=2), encoding="utf-8") + _write_json(path, payload) return gate def _update_release_status(job_id: str) -> dict[str, Any]: + with STATUS_LOCK: + return _update_release_status_locked(job_id) + + +def _update_release_status_locked(job_id: str) -> dict[str, Any]: job_dir = _job_dir(job_id) status = _read_status(job_id) review_passed, reports = _review_passes(job_dir) @@ -598,16 +682,31 @@ async def generate( mapper: str = Form("BeatForge"), seed: int = Form(42), difficulties: str | None = Form(None), - metadata_id: str | None = Form(None), + metadata_id: str | None = Form(None, alias="metadataId"), + mapping_plan: str | None = Form(None, alias="mappingPlan"), + engine: str = Form("premium"), + rl_model: str | None = Form(None, alias="rlModel"), ) -> JSONResponse: suffix = Path(audio.filename or "song.mp3").suffix.casefold() or ".mp3" if suffix not in {".mp3", ".wav", ".ogg", ".egg", ".flac", ".m4a", ".mp4"}: raise HTTPException(400, "Upload MP3, WAV, OGG, FLAC, M4A, or MP4 audio") chosen = parse_studio_difficulties(difficulties) + if engine not in {"premium", "rl"}: + raise HTTPException(400, "Engine must be premium or rl") + if engine == "rl" and not (rl_model or "").strip(): + from beatforge.learning import LEARNING_FILE + from rl.registry import active_checkpoint + try: + rl_model = str(active_checkpoint(LEARNING_FILE.parent / "model_registry").resolve()) + except (OSError, ValueError, KeyError, TypeError) as error: + raise HTTPException(400, "No valid evaluated model is active. Supply a trained checkpoint or initialize the local model registry.") from error + try: + normalized_plan = normalize_mapping_plan(json.loads(mapping_plan) if mapping_plan else None) + except (ValueError, TypeError) as error: + raise HTTPException(400, f"Invalid mapping plan: {error}") from error if metadata_id: _metadata_manifest(metadata_id) JOBS_DIR.mkdir(parents=True, exist_ok=True) - _cleanup_old_jobs() job_id = uuid.uuid4().hex[:12] destination = _job_dir(job_id) destination.mkdir(parents=True) @@ -622,6 +721,9 @@ async def generate( "profile": "official-premium", "difficulties": chosen, "metadataId": metadata_id, + "mappingPlan": normalized_plan, + "engine": engine, + "rlModel": rl_model, "stages": [], "startedAt": time.time(), "elapsed": 0, @@ -632,6 +734,58 @@ async def generate( return JSONResponse({"id": job_id, "status": "queued"}) +@app.get("/api/jobs") +def job_history(limit: int = Query(50, ge=1, le=200)) -> dict[str, Any]: + jobs = [] + if JOBS_DIR.is_dir(): + for directory in JOBS_DIR.iterdir(): + if not directory.is_dir(): + continue + try: + jobs.append(_public_job(_read_status(directory.name))) + except (OSError, ValueError, KeyError, HTTPException): + continue + jobs.sort(key=lambda job: float(job.get("createdAt", job.get("startedAt", 0))), reverse=True) + return {"jobs": jobs[:limit], "total": len(jobs)} + + +@app.post("/api/jobs/{job_id}/retry") +def job_retry(job_id: str, background_tasks: BackgroundTasks) -> dict[str, Any]: + original = _read_status(job_id) + if original.get("status") in ACTIVE_STATES: + raise HTTPException(409, "Cancel or finish this run before retrying") + if original.get("revisionOf"): + raise HTTPException(409, "Retry a section revision from its original map's section controls") + audio = _audio_path(job_id) + new_id = uuid.uuid4().hex[:12] + destination = _job_dir(new_id) + destination.mkdir(parents=True) + shutil.copy2(audio, destination / audio.name) + for filename in ("anchors.json", "approved_palette.json"): + source = _job_dir(job_id) / filename + if source.is_file(): + shutil.copy2(source, destination / filename) + preserved = {key: original[key] for key in ("title", "artist", "mapper", "seed", "profile", "difficulties", "metadataId", "mappingPlan", "engine", "rlModel", "continueUnconfirmed") if key in original} + status = {**preserved, "id": new_id, "retryOf": job_id, "status": "queued", "stages": [], "startedAt": time.time(), "elapsed": 0, "decodePercent": None} + _write_status(new_id, _append_stage(status, "retry", "Retrying with saved audio, confirmed anchors, palette, and mapping settings.")) + background_tasks.add_task(_run_job, new_id) + return {"id": new_id, "status": "queued", "retryOf": job_id} + + +@app.post("/api/jobs/{job_id}/cancel") +def job_cancel(job_id: str) -> dict[str, Any]: + with STATUS_LOCK: + data = _read_status(job_id) + if data.get("status") not in ACTIVE_STATES: + raise HTTPException(409, "This run has already stopped") + (_job_dir(job_id) / "cancel.requested").touch() + state = "cancelled" if data.get("status") == "queued" else "cancelling" + data = _append_stage(data, state, "Cancellation requested; the generation worker is stopping.") + data.update(status=state, localStatus=None) + _write_status(job_id, data) + return {"id": job_id, "status": state} + + @app.get("/api/jobs/{job_id}") def job_status(job_id: str) -> dict[str, Any]: data = _read_status(job_id) @@ -642,10 +796,9 @@ def job_status(job_id: str) -> dict[str, Any]: "release_candidate", }: data = _update_release_status(job_id) - data.pop("trace", None) if data.get("startedAt") and data.get("status") in {"queued", "running", "validating"}: data["elapsed"] = round(time.time() - float(data["startedAt"]), 2) - return data + return _public_job(data) @app.get("/api/jobs/{job_id}/events") @@ -656,7 +809,7 @@ def stream() -> Any: previous = "" deadline = time.time() + 3600 while time.time() < deadline: - data = _read_status(job_id) + data = _public_job(_read_status(job_id)) if data.get("startedAt") and data.get("status") in {"queued", "running", "validating"}: data = {**data, "elapsed": round(time.time() - float(data["startedAt"]), 2)} encoded = json.dumps(data, separators=(",", ":")) @@ -680,15 +833,25 @@ def job_anchors(job_id: str, payload: dict[str, Any], background_tasks: Backgrou if not isinstance(anchors, list) or len(anchors) < 2: raise HTTPException(400, "At least two anchors are required") last_beat = last_sample = None + normalized_anchors = [] for index, anchor in enumerate(anchors): if not isinstance(anchor, dict) or "beat" not in anchor or not ({"sample", "timeSeconds"} & set(anchor)): raise HTTPException(400, f"Anchor {index} requires beat and sample or timeSeconds") - beat = float(anchor["beat"]) - sample = int(anchor.get("sample", round(float(anchor["timeSeconds"]) * 44100))) + try: + beat = float(anchor["beat"]) + raw_sample = float(anchor["sample"]) if "sample" in anchor else float(anchor["timeSeconds"]) * 44100 + if not math.isfinite(beat) or not math.isfinite(raw_sample) or beat < 0 or raw_sample < 0: + raise ValueError("beat and time must be finite nonnegative numbers") + if "sample" in anchor and not raw_sample.is_integer(): + raise ValueError("sample must be an integer") + sample = round(raw_sample) + except (TypeError, ValueError, OverflowError) as error: + raise HTTPException(400, f"Invalid anchor {index}: {error}") from error if last_beat is not None and (beat <= last_beat or sample <= int(last_sample)): raise HTTPException(400, "Anchors must increase strictly in beat and sample") last_beat, last_sample = beat, sample - (_job_dir(job_id) / "anchors.json").write_text(json.dumps({"anchors": anchors}, indent=2), encoding="utf-8") + normalized_anchors.append({**anchor, "beat": beat, "sample": sample}) + _write_json(_job_dir(job_id) / "anchors.json", {"anchors": normalized_anchors}) status["status"] = "queued" status = _append_stage(status, "anchors", "confirmed anchors saved; refitting the beat grid") _write_status(job_id, status) @@ -828,6 +991,8 @@ def job_download(job_id: str) -> FileResponse: data = _read_status(job_id) if data.get("localStatus") not in {"playtest_candidate", "unconfirmed_pack"}: raise HTTPException(409, "Map package is not ready to download") + if _qa_report(_job_dir(job_id) / "map")["errors"]: + raise HTTPException(409, "Map package is blocked by a missing or failed QA report") path = _job_dir(job_id) / "map.zip" if not path.is_file(): raise HTTPException(404, "Map package is missing") @@ -873,11 +1038,10 @@ def import_installed_map(payload: ImportInstalledMap) -> dict[str, Any]: raise HTTPException(409, "Only BeatForge-generated folders with a QA report can be attached") try: info = json.loads(info_path.read_text(encoding="utf-8-sig")) - qa = json.loads(qa_path.read_text(encoding="utf-8")) + qa = _qa_report(source_dir) except (OSError, json.JSONDecodeError) as error: raise HTTPException(409, f"Installed map metadata is unreadable: {error}") from error JOBS_DIR.mkdir(parents=True, exist_ok=True) - _cleanup_old_jobs() job_id = uuid.uuid4().hex[:12] job_dir = _job_dir(job_id) map_dir = job_dir / "map" @@ -1048,13 +1212,16 @@ def playtests(job_id: str, evidence: PlaytestEvidence) -> dict[str, Any]: raise HTTPException(409, "Install and validate a playtest candidate first") if evidence.difficulty not in {"Easy", "Normal", "Hard", "Expert", "ExpertPlus"}: raise HTTPException(400, "Unknown difficulty") + if evidence.difficulty not in data.get("difficulties", STUDIO_DIFFICULTIES): + raise HTTPException(400, "This difficulty is not present in this map") if evidence.speed not in {"slow", "full"}: raise HTTPException(400, "Speed must be slow or full") path = _job_dir(job_id) / "playtests.json" - items = json.loads(path.read_text(encoding="utf-8")) if path.is_file() else [] - items.append(evidence.model_dump(by_alias=True) | {"recordedAt": time.time()}) - path.write_text(json.dumps(items, indent=2), encoding="utf-8") - status = _update_release_status(job_id) + with STATUS_LOCK: + items = json.loads(path.read_text(encoding="utf-8")) if path.is_file() else [] + items.append(evidence.model_dump(by_alias=True) | {"recordedAt": time.time()}) + _write_json(path, items) + status = _update_release_status(job_id) return {"status": status["status"], "releaseGate": status.get("releaseGate"), "evidence": items} @@ -1068,3 +1235,12 @@ def job_click_track(job_id: str) -> FileResponse: if not path.is_file(): raise HTTPException(404, "Click track is not available yet") return FileResponse(path, media_type="audio/wav", filename="click_track.wav") + + +from beatforge.preview import router as preview_router + +app.include_router(preview_router) + +from beatforge.learning import router as learning_router + +app.include_router(learning_router) diff --git a/src/beatforge/cli.py b/src/beatforge/cli.py index c140ef0..d5e9402 100644 --- a/src/beatforge/cli.py +++ b/src/beatforge/cli.py @@ -93,6 +93,9 @@ def main(argv: list[str] | None = None) -> int: p.add_argument("--seed", type=int, default=42) p.add_argument("--anchors", type=Path, default=None) p.add_argument("--palette", type=Path, default=None) + p.add_argument("--mapping-plan", type=Path, help="JSON creative controls shared with Studio") + p.add_argument("--engine", choices=("premium", "rl"), default="premium") + p.add_argument("--rl-model", type=Path, help="Trained checkpoint required by the RL engine") p.add_argument( "--no-critic", action="store_true", @@ -112,6 +115,16 @@ def main(argv: list[str] | None = None) -> int: if args.legacy: return _legacy_main(args) + mapping_plan = None + if args.mapping_plan: + from .mapping_plan import normalize_mapping_plan + try: + mapping_plan = normalize_mapping_plan(json.loads(args.mapping_plan.read_text(encoding="utf-8-sig"))) + except (OSError, ValueError, TypeError) as error: + p.error(f"Invalid mapping plan: {error}") + if args.engine == "rl" and not args.rl_model: + p.error("--engine rl requires --rl-model with a trained checkpoint") + title = args.title or audio.stem out_dir = args.out.expanduser().resolve() map_folder = out_dir / "".join(c if c.isalnum() or c in "-_" else "_" for c in title)[:80] @@ -126,6 +139,9 @@ def main(argv: list[str] | None = None) -> int: seed=args.seed, anchors=args.anchors.expanduser().resolve() if args.anchors else None, palette=args.palette.expanduser().resolve() if args.palette else None, + mapping_plan=mapping_plan, + engine=args.engine, + rl_model=args.rl_model.expanduser().resolve() if args.rl_model else None, progress=lambda stage, detail: print(f" [{stage}] {detail}"), ) print(f"Studio status: {result['status']} (exit {result['returnCode']})") diff --git a/src/beatforge/feedback_guidance.py b/src/beatforge/feedback_guidance.py new file mode 100644 index 0000000..dcafdc7 --- /dev/null +++ b/src/beatforge/feedback_guidance.py @@ -0,0 +1,120 @@ +"""Bounded, opt-in creative suggestions from distinct human playtest reports. + +These helpers do not train a model, verify a headset session, or change a plan. +The caller persists human reports and asks the user to apply suggestedPlan. +""" + +from __future__ import annotations + +from collections import defaultdict +from datetime import datetime, timezone +import math +from typing import Any + +from .mapping_plan import normalize_mapping_plan + + +def _timestamp(value: Any) -> float: + if not isinstance(value, str): + return 0.0 + try: + moment = datetime.fromisoformat(value.replace("Z", "+00:00")) + if moment.tzinfo is None: + moment = moment.replace(tzinfo=timezone.utc) + return moment.timestamp() + except (ValueError, OverflowError): + return 0.0 + + +def _section_key(record: dict[str, Any]) -> tuple[float | None, float | None] | None: + start, end = record.get("startBeat"), record.get("endBeat") + if start is None and end is None: + return (None, None) + if any(isinstance(value, bool) or not isinstance(value, (int, float)) or not math.isfinite(value) for value in (start, end)): + return None + if start < 0 or end <= start: + return None + return (float(start), float(end)) + + +def derive_feedback_adjustment( + records: list[dict[str, Any]], current_plan: dict[str, Any] | None, + *, tester: str = "local", difficulty: str | None = None, +) -> dict[str, Any]: + """One vote per distinct chart, using each chart/section's latest human report.""" + plan = normalize_mapping_plan(current_plan) + latest: dict[tuple[Any, ...], dict[str, Any]] = {} + for record in records: + if not isinstance(record, dict) or record.get("source") != "human" or record.get("schemaVersion", 1) != 1: + continue + if record.get("tester", "local") != tester or (difficulty is not None and record.get("difficulty") != difficulty): + continue + chart_hash = record.get("chartHash") + section = _section_key(record) + if not isinstance(chart_hash, str) or not chart_hash or section is None: + continue + key = (chart_hash, tester, *section) + if key not in latest or _timestamp(record.get("createdAt")) >= _timestamp(latest[key].get("createdAt")): + latest[key] = record + by_chart: dict[str, list[dict[str, Any]]] = defaultdict(list) + for record in latest.values(): + by_chart[record["chartHash"]].append(record) + count = len(by_chart) + result: dict[str, Any] = {"suggestedPlan": plan, "evidenceCount": count, "reasons": [], "ready": count >= 3, "changedFields": {}, "nextActions": []} + if count < 3: + result["reasons"] = [f"Need reports for at least 3 distinct charts from {tester}; found {count}."] + return result + + votes: dict[str, int] = defaultdict(int) + for reports in by_chart.values(): + tags = {tag for report in reports for tag in (report.get("tags") or []) if isinstance(tag, str)} + dense = bool(tags & {"too_dense", "tiring"}) + sparse = "too_sparse" in tags + # Conflicting reports within a chart cancel instead of multiplying votes. + votes["dense"] += int(dense and not sparse) + votes["sparse"] += int(sparse and not dense) + votes["repetitive"] += int("repetitive" in tags) + votes["off_beat"] += int("off_beat" in tags) + readability = [] + for report in reports: + ratings = report.get("ratings", {}) + rating = ratings.get("readability") if isinstance(ratings, dict) else None + if isinstance(rating, (int, float)) and not isinstance(rating, bool) and math.isfinite(rating) and 1 <= rating <= 5: + readability.append(float(rating)) + votes["awkward"] += int("awkward" in tags or bool(readability and sum(readability) / len(readability) <= 2)) + + suggestion = dict(plan) + reasons: list[str] = [] + density_signal = (votes["dense"] - votes["sparse"]) / count + if abs(density_signal) >= 1 / 3 and max(votes["dense"], votes["sparse"]) >= 2: + suggestion["density"] = round(max(0.5, min(1.5, plan["density"] * (1.0 - 0.15 * density_signal))), 4) + direction = "Lower" if density_signal > 0 else "Raise" + reasons.append(f"{direction} density slightly: {votes['dense']} charts were reported too dense or tiring and {votes['sparse']} too sparse.") + if votes["awkward"] >= 2 and votes["awkward"] / count >= 0.5: + suggestion["intensity"] = round(max(0.5, plan["intensity"] * 0.9), 4) + reasons.append(f"Try 10% lower intensity: {votes['awkward']} of {count} charts had awkward movement or low reported readability.") + if votes["repetitive"] >= 2 and votes["repetitive"] / count >= 0.5 and plan["candidateCount"] < 3: + suggestion["candidateCount"] = plan["candidateCount"] + 1 + reasons.append(f"Compare one more safe candidate: {votes['repetitive']} of {count} charts were reported repetitive.") + if votes["off_beat"]: + result["nextActions"].append("Review the sample timing and click track before generating again; density changes cannot repair off-beat notes.") + result["suggestedPlan"] = normalize_mapping_plan(suggestion) + result["changedFields"] = {key: {"from": plan[key], "to": suggestion[key]} for key in ("density", "intensity", "candidateCount") if plan[key] != suggestion[key]} + result["reasons"] = reasons or ["These reports do not establish a consistent creative-control adjustment."] + return result + + +def qa_next_actions(qa: dict[str, Any] | None, timing: dict[str, Any] | None = None) -> list[dict[str, str]]: + """Order concrete next checks from existing machine reports; never certify playability.""" + qa = qa or {} + timing = timing or {} + actions = [] + errors = qa.get("errors") or [] + if errors: + first = errors[0] if isinstance(errors[0], dict) else {} + actions.append({"priority": "blocking", "action": "repair_structure", "detail": str(first.get("message") or first.get("code") or "Resolve the structural validation errors before playtesting.")}) + if timing and timing.get("status") != "timing_verified": + actions.append({"priority": "review", "action": "review_timing", "detail": "Check the click track at the start, middle, and end, then confirm timing anchors."}) + if not errors: + actions.append({"priority": "playtest", "action": "human_playtest", "detail": "Play the chart slowly, then at full speed, and record flow and readability feedback."}) + return actions diff --git a/src/beatforge/learning.py b/src/beatforge/learning.py new file mode 100644 index 0000000..1a72495 --- /dev/null +++ b/src/beatforge/learning.py @@ -0,0 +1,261 @@ +"""Local, explicit human feedback and preferences; no automatic model training.""" + +from __future__ import annotations + +import hashlib +import json +import math +import sys +import threading +import uuid +from datetime import datetime, timezone +from pathlib import Path +from typing import Annotated, Any, Literal + +from fastapi import APIRouter, HTTPException +from pydantic import BaseModel, ConfigDict, Field, StringConstraints, model_validator + +from beatforge.feedback_guidance import derive_feedback_adjustment +from beatforge.mapping_plan import normalize_mapping_plan +from beatforge.premium import ROOT, SCRIPTS +from beatforge.preview import chart_identity, chart_refs, confined, read_json + +router = APIRouter() +LEARNING_FILE = ROOT / "data" / "learning.json" +LEARNING_LOCK = threading.RLock() +Difficulty = Literal["Easy", "Normal", "Hard", "Expert", "ExpertPlus"] +FeedbackTag = Literal["too_dense", "too_sparse", "awkward", "tiring", "repetitive", "off_beat", "good_flow"] +Tester = Annotated[str, StringConstraints(strip_whitespace=True, min_length=1, max_length=120)] +Rating = Annotated[int, Field(strict=True, ge=1, le=5)] + + +class Request(BaseModel): + model_config = ConfigDict(extra="forbid", allow_inf_nan=False) + + +class Ratings(Request): + overall: Rating | None = None + flow: Rating | None = None + readability: Rating | None = None + musicality: Rating | None = None + variety: Rating | None = None + + +class FeedbackRequest(Request): + jobId: str = Field(min_length=1, max_length=80) + difficulty: Difficulty + tester: Tester = "local" + ratings: Ratings = Field(default_factory=Ratings) + tags: list[FeedbackTag] = Field(default_factory=list, max_length=7) + notes: str = Field(default="", max_length=2000) + startBeat: float | None = Field(default=None, ge=0) + endBeat: float | None = Field(default=None, gt=0) + + @model_validator(mode="after") + def meaningful_feedback(self): + if (self.startBeat is None) != (self.endBeat is None): + raise ValueError("Provide both startBeat and endBeat for passage feedback") + if self.startBeat is not None and self.endBeat <= self.startBeat: + raise ValueError("endBeat must follow startBeat") + if not self.ratings.model_dump(exclude_none=True) and not self.tags and not self.notes.strip(): + raise ValueError("Add a rating, tag, or note") + return self + + +class ComparisonRequest(Request): + preferredJob: str = Field(min_length=1, max_length=80) + alternateJob: str = Field(min_length=1, max_length=80) + difficulty: Difficulty + tester: Tester = "local" + notes: str = Field(default="", max_length=2000) + + +class PresetRequest(Request): + name: Annotated[str, StringConstraints(strip_whitespace=True, min_length=1, max_length=80)] + mappingPlan: dict[str, Any] + + +class SuggestionsRequest(Request): + mappingPlan: dict[str, Any] = Field(default_factory=dict) + tester: Tester = "local" + difficulty: Difficulty | None = None + + +class AcceptanceRequest(Request): + tester: Tester = "local" + notes: str = Field(default="", max_length=2000) + + +def _now() -> str: + return datetime.now(timezone.utc).isoformat().replace("+00:00", "Z") + + +def _empty() -> dict[str, Any]: + return {"schemaVersion": 1, "feedback": [], "comparisons": [], "presets": [], "acceptedRevisions": []} + + +def _read_store() -> dict[str, Any]: + if not LEARNING_FILE.is_file(): + return _empty() + try: + data = json.loads(LEARNING_FILE.read_text(encoding="utf-8")) + if not isinstance(data, dict) or data.get("schemaVersion") != 1: + raise ValueError("unsupported learning schema") + for key in ("feedback", "comparisons", "presets", "acceptedRevisions"): + if not isinstance(data.get(key), list) or any(not isinstance(item, dict) for item in data[key]): + raise ValueError("invalid learning records") + return data + except (OSError, ValueError) as error: + raise HTTPException(503, "Saved learning data is unreadable or unsupported. The original file has been preserved.") from error + + +def _write_store(data: dict[str, Any]) -> None: + LEARNING_FILE.parent.mkdir(parents=True, exist_ok=True) + temporary = LEARNING_FILE.with_name(f"learning.{uuid.uuid4().hex}.tmp") + try: + with temporary.open("w", encoding="utf-8") as handle: + json.dump(data, handle, indent=2, allow_nan=False) + handle.write("\n") + handle.flush() + import os + os.fsync(handle.fileno()) + temporary.replace(LEARNING_FILE) + finally: + temporary.unlink(missing_ok=True) + + +def _plan(value: dict[str, Any] | None) -> dict[str, Any]: + try: + return normalize_mapping_plan(value) + except (TypeError, ValueError) as error: + raise HTTPException(422, f"Invalid mapping plan: {error}") from error + + +def _artifact(job_id: str, difficulty: str) -> tuple[dict[str, Any], dict[str, Any]]: + """Bind evidence to disk contents, including playback settings for this chart.""" + from beatforge import api + import soundfile as sf + + status = api._read_status(job_id) + if status.get("status") in api.ACTIVE_STATES: + raise HTTPException(409, "Wait for this map to finish before recording feedback") + folder = api._job_dir(job_id) / "map" + info = read_json(confined(folder, "Info.dat")) + entry = next((item for item in chart_refs(info) if item.get("_difficulty") == difficulty), None) + if entry is None: + raise HTTPException(422, "This difficulty is not present in the selected map") + chart = confined(folder, str(entry["_beatmapFilename"])) + chart_payload = read_json(chart) + audio = confined(folder, str(info.get("_songFilename", ""))) + playback = {"bpm": info.get("_beatsPerMinute"), "offset": info.get("_songTimeOffset", 0), "difficulty": difficulty, "njs": entry.get("_noteJumpMovementSpeed"), "spawnOffset": entry.get("_noteJumpStartBeatOffset")} + digest = hashlib.sha256() + try: + digest.update(json.dumps(playback, sort_keys=True, separators=(",", ":"), allow_nan=False).encode()) + digest.update(json.dumps(chart_payload, sort_keys=True, separators=(",", ":"), allow_nan=False).encode()) + except (TypeError, ValueError) as error: + raise HTTPException(422, "The selected chart contains invalid playback data") from error + audio_digest = hashlib.sha256() + with audio.open("rb") as stream: + for chunk in iter(lambda: stream.read(1024 * 1024), b""): + audio_digest.update(chunk) + try: + duration = sf.info(audio).duration + bpm = float(info.get("_beatsPerMinute", 120)) + if not math.isfinite(bpm) or bpm <= 0: + raise ValueError("Invalid tempo") + except (OSError, RuntimeError, TypeError, ValueError) as error: + raise HTTPException(422, "The selected map has unreadable audio or invalid tempo") from error + snapshot = {"jobId": job_id, "difficulty": difficulty, "chartHash": digest.hexdigest(), "mapHash": chart_identity(folder), "audioHash": audio_digest.hexdigest(), "durationBeats": duration * bpm / 60, "mappingPlan": _plan(status.get("mappingPlan")), "title": str(info.get("_songName", "Untitled")), "engine": str(status.get("engine") or "unknown")} + return snapshot, status + + +@router.get("/api/learning") +def learning_summary() -> dict[str, Any]: + with LEARNING_LOCK: + data = _read_store() + if str(SCRIPTS) not in sys.path: + sys.path.insert(0, str(SCRIPTS)) + from rl.registry import registry_status + + return {"schemaVersion": 1, "feedbackCount": len(data["feedback"]), "comparisonCount": len(data["comparisons"]), "acceptedRevisionCount": len(data["acceptedRevisions"]), "presets": data["presets"], "recentFeedback": data["feedback"][-20:][::-1], "suggestions": derive_feedback_adjustment(data["feedback"], {}, tester="local"), "modelRegistry": registry_status(LEARNING_FILE.parent / "model_registry")} + + +@router.post("/api/learning/suggestions") +def learning_suggestions(request: SuggestionsRequest) -> dict[str, Any]: + plan = _plan(request.mappingPlan) + with LEARNING_LOCK: + data = _read_store() + return derive_feedback_adjustment(data["feedback"], plan, tester=request.tester, difficulty=request.difficulty) + + +@router.post("/api/feedback") +def record_feedback(request: FeedbackRequest) -> dict[str, Any]: + snapshot, _ = _artifact(request.jobId, request.difficulty) + if request.endBeat is not None and request.endBeat > snapshot["durationBeats"] + 1e-6: + raise HTTPException(422, "The feedback passage extends beyond the song") + record = {**snapshot, "schemaVersion": 1, "id": uuid.uuid4().hex[:12], "source": "human", "createdAt": _now(), "tester": request.tester, "ratings": request.ratings.model_dump(exclude_none=True), "tags": sorted(set(request.tags)), "notes": request.notes, "startBeat": request.startBeat, "endBeat": request.endBeat} + with LEARNING_LOCK: + data = _read_store() + data["feedback"].append(record) + _write_store(data) + return {"feedback": record, "feedbackCount": len(data["feedback"])} + + +@router.post("/api/comparisons") +def record_comparison(request: ComparisonRequest) -> dict[str, Any]: + preferred, _ = _artifact(request.preferredJob, request.difficulty) + alternate, _ = _artifact(request.alternateJob, request.difficulty) + if preferred["audioHash"] != alternate["audioHash"]: + raise HTTPException(409, "Compare maps made from the exact same audio file") + if preferred["chartHash"] == alternate["chartHash"]: + raise HTTPException(409, "The selected difficulty charts are identical") + pair = sorted((preferred["chartHash"], alternate["chartHash"])) + identity = hashlib.sha256(json.dumps([preferred["audioHash"], request.difficulty, request.tester.casefold(), pair]).encode()).hexdigest() + record = {"schemaVersion": 1, "id": identity[:20], "source": "human", "createdAt": _now(), "tester": request.tester, "difficulty": request.difficulty, "audioHash": preferred["audioHash"], "chartPair": pair, "preferredJob": request.preferredJob, "alternateJob": request.alternateJob, "preferredChartHash": preferred["chartHash"], "alternateChartHash": alternate["chartHash"], "preferredMappingPlan": preferred["mappingPlan"], "alternateMappingPlan": alternate["mappingPlan"], "notes": request.notes} + with LEARNING_LOCK: + data = _read_store() + replaced = any(item.get("id") == record["id"] for item in data["comparisons"]) + data["comparisons"] = [item for item in data["comparisons"] if item.get("id") != record["id"]] + [record] + _write_store(data) + return {"comparison": record, "comparisonCount": len(data["comparisons"]), "replaced": replaced} + + +@router.post("/api/presets") +def save_preset(request: PresetRequest) -> dict[str, Any]: + preset = {"id": uuid.uuid4().hex[:12], "name": request.name, "mappingPlan": _plan(request.mappingPlan), "createdAt": _now()} + with LEARNING_LOCK: + data = _read_store() + data["presets"].append(preset) + _write_store(data) + return preset + + +def record_accepted_revision(job_id: str, tester: str = "local", notes: str = "") -> dict[str, Any]: + from beatforge import api + + request = AcceptanceRequest(tester=tester, notes=notes) + status = api._read_status(job_id) + revision = status.get("revision") + if not status.get("revisionOf") or not isinstance(revision, dict): + raise HTTPException(409, "Only a generated section revision can be accepted") + if status.get("localStatus") not in {"playtest_candidate", "unconfirmed_pack"} or api._qa_report(api._job_dir(job_id) / "map")["errors"]: + raise HTTPException(409, "Resolve the revision's structural validation failures first") + snapshot, _ = _artifact(job_id, str(revision.get("difficulty", ""))) + record = {**snapshot, "schemaVersion": 1, "id": uuid.uuid4().hex[:12], "source": "human", "createdAt": _now(), "tester": request.tester, "notes": request.notes, "parentJob": status["revisionOf"], "revision": {key: revision.get(key) for key in ("difficulty", "startBeat", "endBeat", "seed", "baseHash")}, "acceptance": "preferred_section_revision"} + with LEARNING_LOCK: + data = _read_store() + data["acceptedRevisions"] = [item for item in data["acceptedRevisions"] if not (item.get("chartHash") == record["chartHash"] and item.get("tester", "").casefold() == request.tester.casefold())] + [record] + _write_store(data) + return record + + +@router.post("/api/jobs/{job_id}/accept-revision") +def accept_revision(job_id: str, request: AcceptanceRequest) -> dict[str, Any]: + return {"acceptedRevision": record_accepted_revision(job_id, request.tester, request.notes)} + + +@router.get("/api/learning/export") +def export_learning() -> dict[str, Any]: + with LEARNING_LOCK: + data = _read_store() + return {"schemaVersion": 1, "kind": "beatforge-human-learning", "exportedAt": _now(), "containsAudio": False, "containsOfficialCorpus": False, "feedback": data["feedback"], "preferences": data["comparisons"], "presets": data["presets"], "acceptedRevisions": data["acceptedRevisions"]} diff --git a/src/beatforge/mapping_plan.py b/src/beatforge/mapping_plan.py new file mode 100644 index 0000000..048dbf1 --- /dev/null +++ b/src/beatforge/mapping_plan.py @@ -0,0 +1,13 @@ +"""Studio access to the portable mapper's single creative-plan contract.""" + +from importlib.util import module_from_spec, spec_from_file_location +from pathlib import Path + +_path = Path(__file__).resolve().parents[2] / "skills" / "beat-saber-mapping" / "scripts" / "mapping_plan.py" +_spec = spec_from_file_location("_beatforge_mapping_plan", _path) +if _spec is None or _spec.loader is None: + raise ImportError("BeatForge mapping plan module is missing") +_module = module_from_spec(_spec) +_spec.loader.exec_module(_module) +normalize_mapping_plan = _module.normalize_mapping_plan +build_section_plan = _module.build_section_plan diff --git a/src/beatforge/premium.py b/src/beatforge/premium.py index 00df110..d7d9a19 100644 --- a/src/beatforge/premium.py +++ b/src/beatforge/premium.py @@ -4,6 +4,7 @@ import json import os +import signal import subprocess import sys import threading @@ -11,9 +12,35 @@ from pathlib import Path from typing import Any, Callable +from .mapping_plan import normalize_mapping_plan + PROGRESS_PREFIX = "BEATFORGE_PROGRESS\t" +class PipelineCancelled(RuntimeError): + """The Studio job cancelled its mapper and child analysis processes.""" + + +def _stop_process_tree(process: subprocess.Popen) -> None: + if process.poll() is not None: + return + if os.name == "nt": + subprocess.run(["taskkill", "/PID", str(process.pid), "/T", "/F"], capture_output=True, check=False) + else: + try: + os.killpg(process.pid, signal.SIGTERM) + except ProcessLookupError: + pass + try: + process.wait(timeout=5) + except subprocess.TimeoutExpired: + if os.name != "nt": + os.killpg(process.pid, signal.SIGKILL) + else: + process.kill() + process.wait() + + def _notify_progress(progress: Callable[..., None], stage: str, detail: str, percent: float | None = None) -> None: kwargs: dict[str, Any] = {} if percent is not None: @@ -57,7 +84,18 @@ def run_premium_pipeline( cover: Path | None = None, allow_unconfirmed: bool = False, difficulties: list[str] | None = None, + mapping_plan: dict[str, Any] | None = None, + engine: str = "premium", + rl_model: Path | None = None, + cancel_requested: Callable[[], bool] | None = None, + analysis_dir: Path | None = None, ) -> dict[str, Any]: + if engine not in {"premium", "rl"}: + raise ValueError("engine must be premium or rl") + if engine == "rl" and (rl_model is None or not Path(rl_model).is_file()): + raise ValueError("The RL engine requires an existing trained checkpoint") + if cancel_requested and cancel_requested(): + raise PipelineCancelled("Mapping cancelled before the pipeline started") command = [ sys.executable, str(SCRIPTS / "generate_map.py"), @@ -88,8 +126,18 @@ def run_premium_pipeline( command += ["--continue-unconfirmed"] for name in difficulties or []: command += ["--difficulty", name] + if mapping_plan is not None: + plan = normalize_mapping_plan(mapping_plan) + plan_path = output / "_beatforge" / "mapping_request.json" + plan_path.parent.mkdir(parents=True, exist_ok=True) + plan_path.write_text(json.dumps(plan, indent=2), encoding="utf-8") + command += ["--mapping-plan", str(plan_path)] + if engine == "rl": + command += ["--engine", "rl", "--rl-model", str(Path(rl_model).resolve())] + if analysis_dir is not None: + command += ["--analysis-dir", str(analysis_dir)] progress("track", f"{title} · {artist}") - progress("pipeline", "starting official-premium generate_map") + progress("pipeline", f"starting {engine} generate_map") environment = os.environ.copy() environment["PYTHONUNBUFFERED"] = "1" process = subprocess.Popen( @@ -102,9 +150,11 @@ def run_premium_pipeline( errors="replace", env=environment, bufsize=1, + start_new_session=os.name != "nt", ) stdout_chunks: list[str] = [] stderr_chunks: list[str] = [] + progress_errors: list[Exception] = [] def consume_stderr() -> None: assert process.stderr is not None @@ -120,7 +170,10 @@ def consume_stderr() -> None: percent = float(parts[3]) except ValueError: percent = None - _notify_progress(progress, parts[1], parts[2], percent) + try: + _notify_progress(progress, parts[1], parts[2], percent) + except Exception as error: + progress_errors.append(error) def consume_stdout() -> None: assert process.stdout is not None @@ -131,9 +184,26 @@ def consume_stdout() -> None: stdout_thread = threading.Thread(target=consume_stdout, daemon=True) stderr_thread.start() stdout_thread.start() - returncode = process.wait() - stderr_thread.join() - stdout_thread.join() + try: + while True: + if progress_errors: + raise progress_errors[0] + if cancel_requested and cancel_requested(): + _stop_process_tree(process) + raise PipelineCancelled("Mapping cancelled") + try: + returncode = process.wait(timeout=0.25) + break + except subprocess.TimeoutExpired: + continue + except BaseException: + _stop_process_tree(process) + raise + finally: + stderr_thread.join() + stdout_thread.join() + if progress_errors: + raise progress_errors[0] stdout_text = "".join(stdout_chunks) stderr_text = "".join(stderr_chunks) status = { diff --git a/src/beatforge/preview.py b/src/beatforge/preview.py new file mode 100644 index 0000000..3cd28f9 --- /dev/null +++ b/src/beatforge/preview.py @@ -0,0 +1,341 @@ +"""Read chart previews and create isolated, fully validated section revisions.""" + +from __future__ import annotations + +import copy +import hashlib +import json +import math +import shutil +import sys +import time +import uuid +from pathlib import Path +from typing import Any + +from fastapi import APIRouter, BackgroundTasks, HTTPException, Query +from fastapi.responses import FileResponse +from pydantic import BaseModel, ConfigDict, Field + +from beatforge.premium import ROOT, SCRIPTS, package_map, summarize_map + +router = APIRouter() +COLLECTIONS = ("colorNotes", "bombNotes", "obstacles", "sliders", "burstSliders", "basicBeatmapEvents", "colorBoostBeatmapEvents", "lightColorEventBoxGroups", "lightRotationEventBoxGroups", "lightTranslationEventBoxGroups", "vfxEventBoxGroups") + + +@router.post("/api/mapping-plan/resolve") +def resolve_mapping_plan(payload: dict[str, Any]) -> dict[str, Any]: + from beatforge.mapping_plan import normalize_mapping_plan + try: + return normalize_mapping_plan(payload) + except ValueError as error: + raise HTTPException(422, str(error)) from error + + +def confined(root: Path, relative: str) -> Path: + path = (root / relative).resolve() + if not path.is_relative_to(root.resolve()) or not path.is_file(): + raise HTTPException(404, "Referenced map file is missing or outside the map folder") + return path + + +def read_json(path: Path) -> dict[str, Any]: + try: + value = json.loads(path.read_text(encoding="utf-8-sig")) + if not isinstance(value, dict): + raise ValueError("expected an object") + return value + except (OSError, ValueError) as error: + raise HTTPException(422, f"Cannot read {path.name}: {error}") from error + + +def chart_refs(info: dict[str, Any]) -> list[dict[str, Any]]: + return [entry for group in info.get("_difficultyBeatmapSets", []) if group.get("_beatmapCharacteristicName") == "Standard" for entry in group.get("_difficultyBeatmaps", [])] + + +def chart_identity(folder: Path) -> str: + info = read_json(confined(folder, "Info.dat")) + names = ["Info.dat", str(info.get("_songFilename", "song.ogg"))] + [str(entry["_beatmapFilename"]) for entry in chart_refs(info)] + digest = hashlib.sha256() + for name in sorted(set(names)): + digest.update(name.encode()) + with confined(folder, name).open('rb') as stream: + for chunk in iter(lambda: stream.read(1048576), b''): + digest.update(chunk) + return digest.hexdigest() + + +def _map(job_id: str) -> tuple[dict[str, Any], Path]: + from beatforge import api + status = api._read_status(job_id) + folder = api._job_dir(job_id) / "map" + if not (folder / "Info.dat").is_file(): + raise HTTPException(404, "This run has no chart to preview yet") + return status, folder + + +def waveform(path: Path, bins: int = 1200) -> tuple[float, list[float]]: + """Read bounded blocks, including multichannel audio, without loading a song in RAM.""" + import numpy as np + import soundfile as sf + with sf.SoundFile(path) as audio: + duration = len(audio) / audio.samplerate + width = max(1, math.ceil(len(audio) / bins)) + peaks: list[float] = [] + while len(peaks) < bins: + block = audio.read(width, dtype="float32", always_2d=True) + if not len(block): + break + peaks.append(round(float(np.max(np.abs(block))), 4)) + return duration, peaks + + +def preview_payload(folder: Path, difficulty: str | None = None) -> dict[str, Any]: + if not (folder / "_beatforge").resolve().is_relative_to(folder.resolve()): + raise HTTPException(404, "Map metadata is outside the map folder") + info = read_json(confined(folder, "Info.dat")) + refs = chart_refs(info) + if not refs: + raise HTTPException(422, "The preview currently supports Standard charts with v2 Info and v3 gameplay") + selected = next((ref for ref in refs if ref.get("_difficulty") == difficulty), None) if difficulty else refs[0] + if not selected: + raise HTTPException(404, "Difficulty is not present in this map") + filename = str(selected["_beatmapFilename"]) + chart = read_json(confined(folder, filename)) + if not str(chart.get("version", "")).startswith("3."): + raise HTTPException(422, "The preview supports v3 gameplay. This chart's format is preserved unchanged.") + audio_path = confined(folder, str(info.get("_songFilename", "song.ogg"))) + with audio_path.open('rb') as stream: + audio_hash = hashlib.file_digest(stream, 'sha256').hexdigest() + cache = folder / "_beatforge" / "preview-waveform.json" + if not cache.resolve().is_relative_to(folder.resolve()): + raise HTTPException(404, "Waveform cache is outside the map folder") + cached: dict[str, Any] = {} + if cache.is_file(): + try: + cached = read_json(cache) + except HTTPException: + pass + cache_duration = cached.get("duration") + cache_peaks = cached.get("waveform") + valid_cache = ( + isinstance(cache_duration, (int, float)) and math.isfinite(cache_duration) and cache_duration > 0 + and isinstance(cache_peaks, list) and 0 < len(cache_peaks) <= 1200 + and all(isinstance(value, (int, float)) and math.isfinite(value) and 0 <= value <= 1 for value in cache_peaks) + ) + if cached.get("audioHash") != audio_hash or not valid_cache: + duration, peaks = waveform(audio_path) + cached = {"audioHash": audio_hash, "duration": duration, "waveform": peaks} + cache.parent.mkdir(parents=True, exist_ok=True) + cache.write_text(json.dumps(cached), encoding="utf-8") + metadata: dict[str, Any] = {} + for key, name in (("analysis", "analysis.json"), ("sections", "sections.json"), ("qa", "qa_report.json"), ("provenance", "provenance.json"), ("mappingPlan", "mapping_plan.json")): + candidate = folder / "_beatforge" / name + metadata[key] = read_json(confined(folder, str(candidate.relative_to(folder)))) if candidate.is_file() else {} + bpm = float(info.get("_beatsPerMinute", 120)) + if not math.isfinite(bpm) or bpm <= 0: + raise HTTPException(422, "Invalid chart tempo") + color_schemes = info.get("_colorSchemes") or [] + scheme_index = int(selected.get("_beatmapColorSchemeIdx", -1)) + scheme = color_schemes[scheme_index].get("colorScheme", {}) if 0 <= scheme_index < len(color_schemes) else {} + if str(SCRIPTS) not in sys.path: + sys.path.insert(0, str(SCRIPTS)) + from timing_export import project_plan_timing + section_plan = metadata["mappingPlan"] + if not section_plan.get("sections"): + section_plan = project_plan_timing(metadata["sections"], metadata["analysis"]) if metadata["analysis"].get("beatGrid") else metadata["sections"] + qa = metadata["qa"] + findings = [item for item in qa.get("errors", []) + qa.get("warnings", []) if item.get("file") in (None, filename) or item.get("difficulty") == selected["_difficulty"]] + return { + "schemaVersion": 1, "title": info.get("_songName", "Untitled"), "artist": info.get("_songAuthorName", ""), + "difficulty": selected["_difficulty"], "difficulties": [item["_difficulty"] for item in refs], "filename": filename, + "bpm": bpm, "offsetSeconds": float(info.get("_songTimeOffset", 0)), "duration": cached["duration"], "waveform": cached["waveform"], + "chart": chart, "chartHash": chart_identity(folder), "audioHash": audio_hash, "sections": section_plan.get("sections", []), "findings": findings, + "mappingPlan": metadata["mappingPlan"], "provenance": {key: metadata["provenance"].get(key) for key in ("seed", "engine", "model", "revision", "mappingPlan")}, + "colors": {"left": scheme.get("saberAColor"), "right": scheme.get("saberBColor")}, + "timingVerified": metadata["analysis"].get("status") == "timing_verified", + } + + +@router.get("/api/jobs/{job_id}/preview") +def job_preview(job_id: str, difficulty: str | None = Query(None)) -> dict[str, Any]: + status, folder = _map(job_id) + result = preview_payload(folder, difficulty) + analysis_path = folder / "_beatforge" / "analysis.json" + has_grid = analysis_path.is_file() and len(read_json(confined(folder, "_beatforge/analysis.json")).get("beatGrid", [])) >= 2 + result.update({"jobId": job_id, "audioUrl": f"/api/jobs/{job_id}/audio", "canRevise": status.get("localStatus") in {"playtest_candidate", "unconfirmed_pack"} and has_grid and not result["chart"].get("bpmEvents") and (folder / "_beatforge" / "sections.json").is_file()}) + return result + + +@router.get("/api/jobs/{job_id}/audio") +def job_audio(job_id: str) -> FileResponse: + _, folder = _map(job_id) + info = read_json(confined(folder, "Info.dat")) + return FileResponse(confined(folder, str(info.get("_songFilename", "song.ogg")))) + + +def interval(item: dict[str, Any], collection: str) -> tuple[float, float]: + start = float(item.get("b", 0)) + end = float(item.get("tb", start)) if collection in {"sliders", "burstSliders"} else start + float(item.get("d", 0)) if collection == "obstacles" else start + if collection.endswith("EventBoxGroups"): + if collection == "vfxEventBoxGroups": + raise ValueError("VFX event box timing cannot yet be revised safely") + offsets = [0.0] + for box in item.get("e", []): + if float(box.get("w", 0)) != 0: + raise ValueError("Distributed lighting timing cannot yet be revised safely") + for key in ("e", "l", "t"): + offsets.extend(float(event.get("b", 0)) for event in box.get(key, [])) + if any(not math.isfinite(offset) or offset < 0 for offset in offsets): + raise ValueError("Invalid relative lighting timing") + end = start + max(offsets) + if not math.isfinite(start) or not math.isfinite(end) or end < start: + raise ValueError("An object has invalid timing") + return start, end + + +def merge_section(original: dict[str, Any], candidate: dict[str, Any], start: float, end: float) -> dict[str, Any]: + """A half-open [start, end) edit. Crossing objects require a wider selection.""" + if not math.isfinite(start) or not math.isfinite(end) or end <= start or start < 0: + raise ValueError("Select an increasing, nonnegative beat range") + result = copy.deepcopy(original) + for collection in COLLECTIONS: + def inside(item: dict[str, Any]) -> bool: + head, tail = interval(item, collection) + overlaps = head < end and tail >= start + included = start <= head < end and tail < end + if tail > head and overlaps and not included: + raise ValueError(f"Selection cuts through {collection} at beat {head:g}. Widen the selection to include the whole object.") + return included + outside = [copy.deepcopy(item) for item in original.get(collection, []) if not inside(item)] + replacement = [copy.deepcopy(item) for item in candidate.get(collection, []) if inside(item)] + if collection in original or replacement: + result[collection] = sorted(outside + replacement, key=lambda item: float(item.get("b", 0))) + return result + + +class RevisionRequest(BaseModel): + model_config = ConfigDict(extra="forbid") + difficulty: str + startBeat: float = Field(ge=0) + endBeat: float = Field(gt=0) + baseHash: str = Field(pattern="^[a-f0-9]{64}$") + seed: int = Field(default=42, ge=0, le=2147483647) + mappingPlan: dict[str, Any] = Field(default_factory=dict) + + +def _run_revision(job_id: str) -> None: + from beatforge import api + if str(SCRIPTS) not in sys.path: + sys.path.insert(0, str(SCRIPTS)) + from choreography import generate_all + from timing_export import project_map_timing, project_plan_timing + from validate_map import validate_package + from beatforge.mapping_plan import normalize_mapping_plan + start_time = time.time() + status = api._read_status(job_id) + request = status["revision"] + folder = api._job_dir(job_id) / "map" + try: + if api._cancel_requested(job_id): + raise InterruptedError("Revision cancelled") + status.update({"status": "running", "stage": "choreography", "detail": "Composing the selected passage from cached audio analysis"}) + api._write_status(job_id, status) + analysis = read_json(folder / "_beatforge" / "analysis.json") + sections = read_json(folder / "_beatforge" / "sections.json") + plan = normalize_mapping_plan(request["mappingPlan"]) + maps, report = generate_all(analysis, sections, request["seed"], difficulties=[request["difficulty"]], mapping_plan=plan) + if api._cancel_requested(job_id): + raise InterruptedError("Revision cancelled") + info = read_json(folder / "Info.dat") + ref = next(item for item in chart_refs(info) if item["_difficulty"] == request["difficulty"]) + path = confined(folder, ref["_beatmapFilename"]) + original = read_json(path) + candidate = project_map_timing(maps[request["difficulty"]], analysis) + merged = merge_section(original, candidate, request["startBeat"], request["endBeat"]) + path.write_text(json.dumps(merged, indent=2) + "\n", encoding="utf-8") + provenance_path = folder / "_beatforge" / "provenance.json" + provenance = read_json(provenance_path) if provenance_path.is_file() else {} + provenance.update({"revision": {**request, "parentJob": status["revisionOf"], "preservedOutsideSelection": True}, "releaseGate": {}, "humanPlaytests": [], "status": "unreviewed_revision"}) + # A revision never inherits reviews or human evidence for the original artifact. + for key in ("reviews", "providerReviews", "playtests", "vrPlaytestPassed", "freshSightReadPassed"): + provenance.pop(key, None) + provenance_path.write_text(json.dumps(provenance, indent=2), encoding="utf-8") + plan_path = folder / "_beatforge" / "mapping_plan.json" + parent_plan = read_json(plan_path) if plan_path.is_file() else {} + updated_plan = project_plan_timing(report.get("mappingPlan", {"controls": plan, "sections": sections.get("sections", [])}), analysis) + updated_plan["appliesTo"] = {"difficulty": request["difficulty"], "startBeat": request["startBeat"], "endBeat": request["endBeat"]} + updated_plan["parentPlan"] = parent_plan + plan_path.write_text(json.dumps(updated_plan, indent=2), encoding="utf-8") + (folder / "_beatforge" / "revision_choreography.json").write_text(json.dumps(report, indent=2), encoding="utf-8") + status.update({"status": "validating", "stage": "validating", "detail": "Checking the complete chart, including both edit boundaries"}) + api._write_status(job_id, status) + qa = validate_package(folder) + qa_payload = qa.to_dict() + (folder / "_beatforge" / "qa_report.json").write_text(json.dumps(qa_payload, indent=2), encoding="utf-8") + if api._cancel_requested(job_id): + raise InterruptedError("Revision cancelled") + if qa.errors: + status.update({"status": "invalid", "localStatus": "invalid", "detail": "The revision failed validation. The original map is unchanged; widen the range or adjust the plan."}) + else: + package_map(folder, api._job_dir(job_id) / "map.zip") + status.update({"status": "review_required", "localStatus": "playtest_candidate" if analysis.get("status") == "timing_verified" else "unconfirmed_pack", "detail": "Section revised and validated. Review the preview before installing.", "timingVerified": analysis.get("status") == "timing_verified"}) + status.update({"stage": "ready", "summary": summarize_map(folder), "qa": {"status": qa.status, "errors": len(qa.errors), "warnings": len(qa.warnings)}}) + except InterruptedError: + status.update({"status": "cancelled", "detail": "Revision cancelled. Original map preserved."}) + except Exception as error: + status.update({"status": "error", "error": str(error), "detail": f"Revision could not complete: {error}. Original map preserved."}) + status["elapsed"] = round(time.time() - start_time, 2) + api._write_status(job_id, status) + + +@router.post("/api/jobs/{job_id}/revise") +def revise_section(job_id: str, request: RevisionRequest, background_tasks: BackgroundTasks) -> dict[str, Any]: + from beatforge import api + from beatforge.mapping_plan import normalize_mapping_plan + status, source = _map(job_id) + if status.get("localStatus") not in {"playtest_candidate", "unconfirmed_pack"}: + raise HTTPException(409, "Start a revision from a completed, structurally valid map") + if api._qa_report(source)["errors"]: + raise HTTPException(409, "The source map no longer passes QA") + if any(path.is_symlink() or getattr(path, 'is_junction', lambda: False)() for path in source.rglob('*')): + raise HTTPException(422, "Revision source must contain regular files, not linked files") + if request.endBeat <= request.startBeat: + raise HTTPException(422, "The end beat must follow the start beat") + if chart_identity(source) != request.baseHash: + raise HTTPException(409, "This map changed. Reload the preview before revising it.") + try: + plan = normalize_mapping_plan(request.mappingPlan) + except ValueError as error: + raise HTTPException(422, str(error)) from error + info = read_json(source / "Info.dat") + ref = next((item for item in chart_refs(info) if item.get("_difficulty") == request.difficulty), None) + if not ref: + raise HTTPException(422, "Select a difficulty in this map") + original = read_json(confined(source, ref["_beatmapFilename"])) + if original.get("bpmEvents"): + raise HTTPException(422, "Section revisions require a constant exported BPM clock. Charts with BPM events are preserved for preview only.") + try: + merge_section(original, original, request.startBeat, request.endBeat) + except ValueError as error: + raise HTTPException(422, str(error)) from error + for required in ("analysis.json", "sections.json"): + if not (source / "_beatforge" / required).is_file(): + raise HTTPException(409, "This imported map has no cached analysis for regeneration") + analysis = read_json(source / "_beatforge" / "analysis.json") + if len(analysis.get("beatGrid", [])) < 2: + raise HTTPException(409, "This map has no adopted beat/sample grid for regeneration") + if request.endBeat * 60 / float(info["_beatsPerMinute"]) > float(analysis.get("durationSeconds", 0)) + 1e-6: + raise HTTPException(422, "The selected range extends beyond the song") + new_id = uuid.uuid4().hex[:12] + destination = api._job_dir(new_id) + destination.mkdir(parents=True) + shutil.copytree(source, destination / "map") + for path in api._job_dir(job_id).glob("input.*"): + shutil.copy2(path, destination / path.name) + revision = request.model_dump() + revision["mappingPlan"] = plan + new_status = {"id": new_id, "status": "queued", "title": status.get("title"), "artist": status.get("artist"), "mapper": status.get("mapper", "BeatForge"), "seed": request.seed, "difficulties": status.get("difficulties", []), "startedAt": time.time(), "stages": [], "revisionOf": job_id, "revision": revision, "mappingPlan": plan, "installed": False, "releaseGate": {}} + api._write_status(new_id, new_status) + background_tasks.add_task(_run_revision, new_id) + return {"id": new_id, "status": "queued", "revisionOf": job_id} diff --git a/tests/test_api.py b/tests/test_api.py index e416e30..f24c6ab 100644 --- a/tests/test_api.py +++ b/tests/test_api.py @@ -15,6 +15,19 @@ from test_pipeline import write_click_track +@pytest.fixture(autouse=True) +def isolate_studio_files(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> None: + """Endpoint tests must never touch the user's real jobs or game library.""" + from beatforge import api + + levels = tmp_path / "test-custom-levels" + levels.mkdir() + monkeypatch.setenv("BEATSABER_CUSTOM_LEVELS", str(levels)) + monkeypatch.setattr(api, "JOBS_DIR", tmp_path / "test-jobs") + monkeypatch.setattr(api, "IMPORTS_DIR", tmp_path / "test-imports") + monkeypatch.setattr(api, "run_premium_pipeline", lambda **kwargs: {"status": "needs_anchors", "payload": {"message": "test fixture"}}) + + @pytest.fixture(scope="module") def client() -> TestClient: return TestClient(app) @@ -376,7 +389,7 @@ def unexpected_install_error(*args: object, **kwargs: object) -> Path: assert len(dl.content) > 1000 -def test_studio_states_are_the_ten_consumed_release_states() -> None: +def test_studio_states_include_recoverable_terminal_states() -> None: from beatforge.api import STUDIO_STATES, TERMINAL_STATES assert STUDIO_STATES == ( @@ -390,9 +403,11 @@ def test_studio_states_are_the_ten_consumed_release_states() -> None: "release_candidate", "invalid", "error", + "interrupted", + "cancelled", ) assert "validating" not in TERMINAL_STATES - assert len(STUDIO_STATES) == 10 + assert len(STUDIO_STATES) == 12 def test_click_track_serves_analysis_wav_only( @@ -453,6 +468,9 @@ def test_unconfirmed_pack_auto_installs_into_custom_levels( map_dir.mkdir(parents=True) (job_dir / "input.wav").write_bytes(b"RIFF") (map_dir / "Info.dat").write_text('{"_songName":"Draft"}', encoding="utf-8") + reports = map_dir / "_beatforge" + reports.mkdir() + (reports / "qa_report.json").write_text('{"status":"playtest_candidate","errors":[],"warnings":[]}', encoding="utf-8") api._write_status( job_id, { @@ -468,7 +486,7 @@ def test_unconfirmed_pack_auto_installs_into_custom_levels( ) def fake_pipeline(**kwargs: object) -> dict: - return {"status": "invalid", "payload": {"message": "unconfirmed"}} + return {"status": "playtest_candidate", "payload": {"message": "unconfirmed"}} monkeypatch.setattr(api, "run_premium_pipeline", fake_pipeline) monkeypatch.setattr(api, "summarize_map", lambda _path: {"difficulties": {}}) @@ -493,6 +511,9 @@ def test_unconfirmed_pack_can_be_downloaded( job_dir = tmp_path / job_id job_dir.mkdir() (job_dir / "map.zip").write_bytes(b"PK" + b"\x00" * 1200) + reports = job_dir / "map" / "_beatforge" + reports.mkdir(parents=True) + (reports / "qa_report.json").write_text('{"status":"playtest_candidate","errors":[],"warnings":[]}', encoding="utf-8") api._write_status( job_id, {"id": job_id, "status": "invalid", "localStatus": "unconfirmed_pack", "title": "Draft"}, @@ -749,7 +770,7 @@ def __init__(self, code: int) -> None: self.stdout = io.StringIO(json.dumps({"status": "x", "message": "pipeline"})) self.stderr = io.StringIO("BEATFORGE_PROGRESS\tanalysis\tdecoding audio\n") - def wait(self) -> int: + def wait(self, timeout: float | None = None) -> int: return self.returncode def run_with(code: int) -> str: @@ -789,7 +810,7 @@ def __init__(self, *args: object, **kwargs: object) -> None: "BEATFORGE_PROGRESS\tchoreography\tsolving joint CP-SAT for ExpertPlus\n" ) - def wait(self) -> int: + def wait(self, timeout: float | None = None) -> int: return self.returncode seen: list[tuple[str, str]] = [] diff --git a/tests/test_browser.py b/tests/test_browser.py index b17f6d8..84fb351 100644 --- a/tests/test_browser.py +++ b/tests/test_browser.py @@ -4,6 +4,7 @@ import threading import time +import socket from pathlib import Path import pytest @@ -12,7 +13,6 @@ ROOT = Path(__file__).resolve().parents[1] -STUDIO_PORT = 18765 def _playwright_chromium(): @@ -28,12 +28,22 @@ def _playwright_chromium(): @pytest.fixture(scope="module") -def studio_url(): +def studio_url(tmp_path_factory): import uvicorn + from beatforge import api, learning - config = uvicorn.Config(app, host="127.0.0.1", port=STUDIO_PORT, log_level="warning") + isolated = tmp_path_factory.mktemp("browser-studio") + patches = pytest.MonkeyPatch() + for field in ("JOBS_DIR", "IMPORTS_DIR", "METADATA_DIR"): + patches.setattr(api, field, isolated / field.lower()) + patches.setattr(learning, "LEARNING_FILE", isolated / "learning.json") + patches.setattr(api, "find_custom_levels", lambda: None) + sock = socket.socket() + sock.bind(("127.0.0.1", 0)) + port = sock.getsockname()[1] + config = uvicorn.Config(app, host="127.0.0.1", port=port, log_level="warning") server = uvicorn.Server(config) - thread = threading.Thread(target=server.run, daemon=True) + thread = threading.Thread(target=server.run, kwargs={"sockets": [sock]}, daemon=True) thread.start() deadline = time.time() + 8 while time.time() < deadline: @@ -42,12 +52,14 @@ def studio_url(): time.sleep(0.05) if not server.started: pytest.skip("could not bind the headless studio server") - yield f"http://127.0.0.1:{STUDIO_PORT}" + yield f"http://127.0.0.1:{port}" server.should_exit = True thread.join(timeout=4) + sock.close() + patches.undo() -@pytest.fixture(scope="module") +@pytest.fixture def page(studio_url: str): playwright, browser = _playwright_chromium() context = browser.new_context(viewport={"width": 1280, "height": 900}) @@ -112,8 +124,11 @@ def test_playwright_mobile_tablet_and_200_percent_zoom(studio_url: str) -> None: for options in cases: playwright, browser, context, opened = _open_studio(studio_url, **options) try: - assert opened.locator("article.connector").count() == 3 + assert opened.locator("#previewPanel").is_visible() + assert opened.locator("#historyPanel").is_visible() + assert opened.locator("#learningPanel").is_visible() assert opened.locator("#generate").is_visible() + assert opened.evaluate("document.documentElement.scrollWidth <= window.innerWidth + 1") assert opened.locator('img[alt="BeatForge"]').count() == 1 opened.locator("#generate").click() assert opened.locator("#statusTitle").inner_text() == "Select a mastered audio file first." @@ -128,7 +143,7 @@ def test_playwright_keyboard_reaches_generate_and_refuses_without_audio(studio_u try: opened.locator("body").click(position={"x": 8, "y": 8}) seen: list[str] = [] - for _ in range(24): + for _ in range(45): opened.keyboard.press("Tab") focused = opened.evaluate("document.activeElement && document.activeElement.id") if focused: @@ -171,6 +186,7 @@ def test_playwright_offline_and_unconfigured_codex_state(studio_url: str) -> Non assert page.locator("#gameStatus").inner_text() == "Beat Saber check unavailable" page.evaluate("() => { document.getElementById('timingAi').hidden = false; }") page.locator('.timing-ai-btn[data-provider="codex"]').click() + page.locator("#providerDialog").wait_for(state="visible") assert page.locator("#providerDialog").is_visible() assert "OpenAI Codex setup" in page.locator("#providerName").inner_text() assert "never returns it to this page" in page.locator("#providerDialog").inner_text() @@ -208,13 +224,16 @@ def test_playwright_webmcp_mock_registers_and_runs_collaboration_loop(studio_url await call('set_mapping_plan')({ title: 'Agent Shaped Rain', creativeBrief: 'Readable Expert chorus with a bright syncopated lift.', - difficulties: ['Normal', 'Hard', 'Expert'] + difficulties: ['Normal', 'Hard', 'Expert'], + mappingPlan: {density: 0.75, noBombs: true, style: 'flow'} }); const generated = JSON.parse(await call('generate_beatmap')({ reason: 'Turn the shared brief into a preview.' })); const evidence = JSON.parse(await call('record_human_playtest')({ difficulty: 'Expert', speed: 'full', passed: true, tester: 'Human tester', notes: 'Real person supplied this evidence.' })); const context = JSON.parse(await call('get_studio_context')({ includeActivity: true })); + const preview = JSON.parse(await call('get_chart_preview')({})); + const learning = JSON.parse(await call('get_learning_summary')({})); return { names: tools.map(tool => tool.name).sort(), title: document.getElementById('title').value, @@ -222,6 +241,9 @@ def test_playwright_webmcp_mock_registers_and_runs_collaboration_loop(studio_url generated, evidence, context, + preview, + learning, + planSchema: tools.find(tool => tool.name === 'set_mapping_plan').inputSchema.properties.mappingPlan, activity: document.getElementById('agentActivity').innerText }; } @@ -230,16 +252,27 @@ def test_playwright_webmcp_mock_registers_and_runs_collaboration_loop(studio_url assert result["names"] == sorted( [ "get_studio_context", + "find_song_metadata", + "import_song_preview", "set_mapping_plan", "load_collaboration_demo", "generate_beatmap", "review_current_beatmap", "record_human_playtest", + "get_chart_preview", + "get_learning_summary", + "record_mapping_feedback", ] ) assert result["title"] == "Agent Shaped Rain" assert "Readable Expert chorus" in result["brief"] assert result["generated"]["mode"] == "rights-safe-demo" + assert result["context"]["mappingPlan"]["mappingPlan"]["density"] == 0.75 + assert result["context"]["mappingPlan"]["mappingPlan"]["noBombs"] is True + assert result["preview"]["notes"] > 0 + assert result["learning"]["feedbackCount"] == 0 + assert result["planSchema"]["additionalProperties"] is False + assert result["planSchema"]["properties"]["density"]["minimum"] == 0.5 assert result["evidence"]["releaseGate"]["evidenceCount"] == 1 assert result["context"]["job"]["demo"] is True assert "Agent called generate_beatmap" in result["activity"] diff --git a/tests/test_demo_assets.py b/tests/test_demo_assets.py new file mode 100644 index 0000000..1498ce7 --- /dev/null +++ b/tests/test_demo_assets.py @@ -0,0 +1,139 @@ +"""The public demo must show the packaged charts and the real decoded audio.""" + +from __future__ import annotations + +import hashlib +import json +import sys +import zipfile +from pathlib import Path + +import numpy as np +import soundfile as sf +import pytest + +ROOT = Path(__file__).resolve().parents[1] +DEMO = ROOT / "web" / "assets" / "demo" +SCRIPTS = ROOT / "skills" / "beat-saber-mapping" / "scripts" +if str(SCRIPTS) not in sys.path: + sys.path.insert(0, str(SCRIPTS)) + +from validate_map import validate_package +from timing_export import ExportClock, project_map_timing, project_plan_timing +from mapping_plan import build_section_plan +from beatforge.preview import chart_identity, waveform + + +def test_demo_audio_decodes_to_the_advertised_30_seconds() -> None: + preview = json.loads((DEMO / "preview.json").read_text()) + audio, sample_rate = sf.read(DEMO / "song.ogg", always_2d=True) + assert sample_rate == 44100 + assert audio.shape == (30 * 44100, 2) + assert preview["duration"] == len(audio) / sample_rate == 30.0 + assert float(np.sqrt(np.mean(audio ** 2))) > 0.02 + assert float(np.max(np.abs(audio))) < 1 + assert len(preview["waveform"]) >= 1000 + assert max(preview["waveform"]) > 0.2 + assert len(set(preview["waveform"])) > 100 + duration, peaks = waveform(DEMO / "song.ogg") + assert preview["duration"] == duration + assert preview["waveform"] == peaks + assert preview["provenance"]["audio"]["sha256"] == hashlib.sha256((DEMO / "song.ogg").read_bytes()).hexdigest() + + +def test_demo_zip_and_displayed_charts_pass_canonical_validation(tmp_path: Path) -> None: + preview = json.loads((DEMO / "preview.json").read_text()) + with zipfile.ZipFile(DEMO / "map.zip") as archive: + assert archive.read("song.ogg") == (DEMO / "song.ogg").read_bytes() + archive.extractall(tmp_path) + info = json.loads(archive.read("Info.dat")) + refs = info["_difficultyBeatmapSets"][0]["_difficultyBeatmaps"] + assert len(refs) == 5 + for entry in refs: + name = entry["_difficulty"] + chart = json.loads(archive.read(entry["_beatmapFilename"])) + assert preview["charts"][name] == chart + assert preview["summary"]["difficulties"][name]["notes"] == len(chart["colorNotes"]) > 0 + for field, collection in (("arcs", "sliders"), ("chains", "burstSliders"), ("bombs", "bombNotes"), ("walls", "obstacles")): + assert preview["summary"]["difficulties"][name][field] == len(chart[collection]) + assert preview["chart"] == preview["charts"]["Hard"] + assert preview["difficulty"] == "Hard" + validation = validate_package(tmp_path) + assert not validation.errors, validation.to_dict() + assert validation.status == "playtest_candidate" + assert preview["qa"]["errors"] == [] + assert preview["qa"]["metrics"]["totals"] == validation.to_dict()["metrics"]["totals"] + assert preview["chartHash"] == chart_identity(tmp_path) + assert preview["audioHash"] == hashlib.sha256((tmp_path / "song.ogg").read_bytes()).hexdigest() + + +def test_demo_timing_provenance_describes_the_authored_score_honestly() -> None: + preview = json.loads((DEMO / "preview.json").read_text()) + with zipfile.ZipFile(DEMO / "map.zip") as archive: + analysis = json.loads(archive.read("_beatforge/analysis.json")) + provenance = json.loads(archive.read("_beatforge/provenance.json")) + assert analysis["source"] == preview["source"] == "synthetic-authored-score" + assert analysis["timingEvidence"]["authoredSampleClock"] is True + assert analysis["timingEvidence"]["trackerAnalysisPerformed"] is False + assert analysis["timingEvidence"]["humanListeningVerified"] is False + for event in analysis["events"]: + assert event["sample"] == round(event["beat"] * 60 / analysis["bpm"] * analysis["sampleRate"]) + assert 0 <= event["sample"] < round(analysis["durationSeconds"] * analysis["sampleRate"]) + assert provenance["officialCorpusUsed"] is False + assert provenance["humanPlaytests"] == [] + assert provenance["releaseGate"]["fullSpeedVrPlaytest"] is False + assert provenance["releaseGate"]["freshSightRead"] is False + assert provenance["releaseGate"]["structuralInspection"] is True + assert preview["sections"][0]["startBeat"] == 0 + assert preview["sections"][-1]["endBeat"] == 60 + + +def test_demo_grid_and_raw_sections_are_ready_for_safe_local_revision() -> None: + preview = json.loads((DEMO / "preview.json").read_text()) + with zipfile.ZipFile(DEMO / "map.zip") as archive: + analysis = json.loads(archive.read("_beatforge/analysis.json")) + grid = json.loads(archive.read("_beatforge/beat_grid.json")) + sections = json.loads(archive.read("_beatforge/sections.json")) + plan = json.loads(archive.read("_beatforge/mapping_plan.json")) + report = json.loads(archive.read("_beatforge/choreography_report.json")) + provenance = json.loads(archive.read("_beatforge/provenance.json")) + assert analysis["durationSamples"] == 30 * 44100 + assert grid["beats"] == analysis["beatGrid"] + assert grid["tempoRegions"] == analysis["tempoRegions"] + assert grid["sampleRate"] == analysis["sampleRate"] == 44100 + rows = grid["beats"] + assert len(rows) >= 60 + for row in rows: + assert row["sample"] == round(row["beat"] * 60 / analysis["bpm"] * analysis["sampleRate"]) + assert 0 <= row["sample"] < analysis["durationSamples"] + assert row["timeSeconds"] == row["sample"] / analysis["sampleRate"] + assert all(right["sample"] > left["sample"] and right["beat"] > left["beat"] for left, right in zip(rows, rows[1:])) + assert "controls" not in sections + assert all("authoredStartBeat" not in section and "density" not in section for section in sections["sections"]) + expected = project_plan_timing(build_section_plan(analysis, sections, plan["controls"]), analysis) + assert plan == expected == report["mappingPlan"] == provenance["mappingPlan"] == preview["mappingPlan"] + assert preview["sections"] == plan["sections"] + assert plan["sections"][-1]["endSeconds"] == preview["duration"] + + +def test_demo_gameplay_and_hold_endpoints_use_the_exported_sample_clock_once() -> None: + preview = json.loads((DEMO / "preview.json").read_text()) + with zipfile.ZipFile(DEMO / "map.zip") as archive: + analysis = json.loads(archive.read("_beatforge/analysis.json")) + clock = ExportClock(analysis) + for event in analysis["events"]: + exported_seconds = clock.beat(event["beat"]) * 60 / analysis["bpm"] + assert abs(exported_seconds * analysis["sampleRate"] - event["sample"]) < 0.001 + for chart in preview["charts"].values(): + assert chart["customData"]["_beatforgeTiming"]["clock"] == "constant-bpm-sample-projection" + assert chart["bpmEvents"] == [] + for collection in ("colorNotes", "bombNotes", "sliders", "burstSliders"): + for item in chart.get(collection, []): + for field in ("b", "tb"): + if field not in item: + continue + sample = item[field] * 60 / analysis["bpm"] * analysis["sampleRate"] + assert abs(sample - round(sample)) < 0.001 + assert 0 <= sample < analysis["durationSamples"] + with pytest.raises(ValueError, match="already been projected"): + project_map_timing(chart, analysis) diff --git a/tests/test_feedback_guidance.py b/tests/test_feedback_guidance.py new file mode 100644 index 0000000..e716b6b --- /dev/null +++ b/tests/test_feedback_guidance.py @@ -0,0 +1,52 @@ +from beatforge.feedback_guidance import derive_feedback_adjustment, qa_next_actions + + +def report(chart, tags=(), **changes): + return {"schemaVersion": 1, "chartHash": chart, "tester": "local", "difficulty": "Expert", "source": "human", "tags": list(tags), "createdAt": "2026-09-12T01:00:00Z", **changes} + + +def test_duplicates_and_sections_do_not_inflate_readiness(): + records = [report("one", ["too_dense"]) for _ in range(6)] + records.append(report("one", ["too_dense"], startBeat=4, endBeat=8)) + result = derive_feedback_adjustment(records, {}) + assert result["evidenceCount"] == 1 + assert result["ready"] is False + assert result["changedFields"] == {} + + +def test_latest_report_replaces_old_vote_and_other_testers_are_excluded(): + records = [report("one", ["too_dense"]), report("one", ["too_sparse"], createdAt="2026-09-12T02:00:00Z"), report("two", ["too_sparse"]), report("three", ["too_sparse"]), report("four", ["too_dense"], tester="other")] + result = derive_feedback_adjustment(records, {"density": 1.0}, difficulty="Expert") + assert result["ready"] and result["evidenceCount"] == 3 + assert result["suggestedPlan"]["density"] == 1.15 + + +def test_suggestion_is_bounded_and_preserves_manual_controls_and_input(): + plan = {"density": 0.55, "style": "tech", "noBombs": True, "dominantInstrument": "bass", "sectionOverrides": [{"id": "section-001", "density": 1.4}]} + records = [report(str(i), ["too_dense", "awkward", "repetitive"]) for i in range(3)] + result = derive_feedback_adjustment(records, plan) + suggested = result["suggestedPlan"] + assert suggested["density"] == 0.5 + assert suggested["intensity"] == 0.9 + assert suggested["candidateCount"] == 3 + assert all(suggested[key] == plan[key] for key in ("style", "noBombs", "dominantInstrument", "sectionOverrides")) + assert plan["density"] == 0.55 + + +def test_automated_or_wrong_difficulty_reports_are_not_human_evidence(): + records = [report(str(i), ["too_dense"], source="automated") for i in range(3)] + records += [report("easy", ["too_dense"], difficulty="Easy")] + assert derive_feedback_adjustment(records, {}, difficulty="Expert")["evidenceCount"] == 0 + + +def test_mixed_signals_make_no_density_adjustment_and_timing_gets_review_action(): + records = [report("one", ["too_dense"]), report("two", ["too_sparse"]), report("three", ["off_beat"])] + result = derive_feedback_adjustment(records, {}) + assert result["changedFields"] == {} + assert result["nextActions"] and "timing" in result["nextActions"][0] + + +def test_qa_prioritizes_known_blockers_and_never_marks_a_chart_playtested(): + actions = qa_next_actions({"errors": [{"code": "HANDCLAP", "message": "Hands collide"}]}, {"status": "needs_anchors"}) + assert [item["action"] for item in actions] == ["repair_structure", "review_timing"] + assert qa_next_actions({"errors": []}, {"status": "timing_verified"})[0]["action"] == "human_playtest" diff --git a/tests/test_job_lifecycle.py b/tests/test_job_lifecycle.py new file mode 100644 index 0000000..1190eff --- /dev/null +++ b/tests/test_job_lifecycle.py @@ -0,0 +1,216 @@ +"""Studio contracts, failed gates, and recovery without a real game installation.""" + +from __future__ import annotations + +import json +import os +from pathlib import Path + +import pytest +from fastapi.testclient import TestClient + +from beatforge import api + + +@pytest.fixture +def studio(tmp_path: Path, monkeypatch: pytest.MonkeyPatch): + monkeypatch.setattr(api, "JOBS_DIR", tmp_path / "jobs") + monkeypatch.setattr(api, "METADATA_DIR", tmp_path / "metadata") + monkeypatch.setattr(api, "IMPORTS_DIR", tmp_path / "imports") + monkeypatch.setattr(api, "find_custom_levels", lambda: None) + monkeypatch.setattr(api, "_run_job", api._run_job) + def forbid_install(*args, **kwargs): + raise AssertionError("This test must not install a map") + monkeypatch.setattr(api, "_install_pack_to_custom_levels", forbid_install) + return TestClient(api.app) + + +def saved_job(identifier: str, state: str = "needs_anchors", **fields) -> Path: + api._write_status(identifier, {"id": identifier, "status": state, "stages": [], **fields}) + directory = api._job_dir(identifier) + (directory / "input.wav").write_bytes(b"saved-audio") + return directory + + +def test_browser_metadata_and_creative_plan_reach_generation(studio, monkeypatch: pytest.MonkeyPatch) -> None: + metadata_id = "0123456789abcdef0123" + api.METADATA_DIR.mkdir() + cover = api.METADATA_DIR / "cover.png" + cover.write_bytes(b"cover") + (api.METADATA_DIR / f"{metadata_id}.json").write_text(json.dumps({"_cachePath": str(cover)})) + observed = {} + def pipeline(**kwargs): + observed.update(kwargs) + return {"status": "needs_anchors", "payload": {}} + monkeypatch.setattr(api, "run_premium_pipeline", pipeline) + response = studio.post("/api/generate", files={"audio": ("song.wav", b"RIFF", "audio/wav")}, data={ + "metadataId": metadata_id, + "mappingPlan": json.dumps({"brief": "Bass led", "density": 0.8}), + "difficulties": "Easy,Hard", + }) + assert response.status_code == 200 + job = api._read_status(response.json()["id"]) + assert job["metadataId"] == metadata_id + assert observed["cover"] == cover + assert observed["mapping_plan"]["density"] == 0.8 + assert observed["mapping_plan"]["brief"] == "Bass led" + assert observed["difficulties"] == ["Easy", "Hard"] + assert observed["allow_unconfirmed"] is False + + +@pytest.mark.parametrize("plan", ["not-json", "[]", '{"density": 900}', '{"noWalls": "false"}']) +def test_invalid_mapping_plan_is_rejected_before_creating_job(studio, plan: str) -> None: + response = studio.post("/api/generate", files={"audio": ("song.wav", b"RIFF", "audio/wav")}, data={"mappingPlan": plan}) + assert response.status_code == 400 + assert not api.JOBS_DIR.exists() + + +@pytest.mark.parametrize("anchors,expected", [ + ([{"beat": 0, "sample": 0}, {"beat": 1, "sample": 22050}], [0, 22050]), + ([{"beat": 0, "timeSeconds": 0}, {"beat": 1, "timeSeconds": 0.5}], [0, 22050]), +]) +def test_both_anchor_formats_are_normalized_and_queued(studio, monkeypatch: pytest.MonkeyPatch, anchors, expected) -> None: + directory = saved_job("anchors") + scheduled = [] + monkeypatch.setattr(api, "_run_job", scheduled.append) + response = studio.post("/api/jobs/anchors/anchors", json={"anchors": anchors}) + assert response.status_code == 200 + assert scheduled == ["anchors"] + assert [item["sample"] for item in json.loads((directory / "anchors.json").read_text())["anchors"]] == expected + + +@pytest.mark.parametrize("anchor", [ + {"beat": 1, "sample": -1}, {"beat": "nan", "sample": 1}, + {"beat": 1, "timeSeconds": "inf"}, {"beat": "bad", "sample": 1}, + {"beat": 1, "sample": 2.5}, {"beat": 1, "timeSeconds": None}, +]) +def test_bad_anchors_return_actionable_400(studio, anchor) -> None: + directory = saved_job("badanchors") + response = studio.post("/api/jobs/badanchors/anchors", json={"anchors": [{"beat": 0, "sample": 0}, anchor]}) + assert response.status_code == 400 + assert "anchor" in response.json()["detail"].lower() + assert not (directory / "anchors.json").exists() + assert api._read_status("badanchors")["status"] == "needs_anchors" + + +@pytest.mark.parametrize("report", [None, "{broken", {}, {"status": "invalid", "errors": [], "warnings": []}, {"status": "invalid", "errors": ["collision"], "warnings": []}]) +def test_failed_or_missing_qa_never_packages_or_installs(studio, monkeypatch: pytest.MonkeyPatch, report) -> None: + directory = saved_job("failedqa", "queued") + reports = directory / "map" / "_beatforge" + reports.mkdir(parents=True) + (directory / "map" / "Info.dat").write_text("{}") + if report is not None: + (reports / "qa_report.json").write_text(report if isinstance(report, str) else json.dumps(report)) + (reports / "analysis.json").write_text('{"status":"timing_verified"}') + monkeypatch.setattr(api, "run_premium_pipeline", lambda **kwargs: {"status": "invalid"}) + api._run_job("failedqa") + status = api._read_status("failedqa") + assert status["status"] == status["localStatus"] == "invalid" + assert status["qa"]["errors"] > 0 + assert not status.get("installed") + assert not (directory / "map.zip").exists() + assert studio.get("/api/jobs/failedqa/download").status_code == 409 + assert studio.post("/api/jobs/failedqa/install").status_code == 409 + + +def test_download_rechecks_report_even_if_saved_status_claims_ready(studio) -> None: + directory = saved_job("staleready", "review_required", localStatus="playtest_candidate") + (directory / "map.zip").write_bytes(b"old-package") + assert studio.get("/api/jobs/staleready/download").status_code == 409 + + +def test_startup_marks_abandoned_workers_without_deleting_old_history(studio) -> None: + running = saved_job("running", "running") + cancelled = saved_job("cancelled", "cancelling") + (cancelled / "cancel.requested").touch() + ready = saved_job("oldready", "review_required", createdAt=1) + os.utime(ready, (1, 1)) + api._on_startup() + assert api._read_status("running")["status"] == "interrupted" + assert api._read_status("cancelled")["status"] == "cancelled" + assert api._read_status("oldready")["status"] == "review_required" + assert (running / "input.wav").read_bytes() == b"saved-audio" + assert ready.is_dir() + history = studio.get("/api/jobs").json() + assert history["total"] == 3 + assert history["jobs"][-1]["id"] == "oldready" + assert all(job["canRetry"] for job in history["jobs"]) + + +def test_retry_preserves_confirmed_inputs_without_stale_output_or_evidence(studio, monkeypatch: pytest.MonkeyPatch) -> None: + original = saved_job("original", "interrupted", title="Song", seed=7, difficulties=["Hard"], mappingPlan={"density": 0.8}, metadataId="track", engine="premium") + (original / "anchors.json").write_text('{"anchors":[]}') + (original / "approved_palette.json").write_text('{"approved":true}') + (original / "playtests.json").write_text('[{"passed":true}]') + (original / "map.zip").write_bytes(b"stale") + scheduled = [] + monkeypatch.setattr(api, "_run_job", scheduled.append) + result = studio.post("/api/jobs/original/retry") + assert result.status_code == 200 + identifier = result.json()["id"] + assert identifier != "original" + assert scheduled == [identifier] + retry = api._job_dir(identifier) + for name in ("input.wav", "anchors.json", "approved_palette.json"): + assert (retry / name).read_bytes() == (original / name).read_bytes() + assert not (retry / "map.zip").exists() + assert not (retry / "playtests.json").exists() + status = api._read_status(identifier) + assert status["retryOf"] == "original" + assert status["mappingPlan"] == {"density": 0.8} + assert status["difficulties"] == ["Hard"] + assert status["seed"] == 7 + assert status["metadataId"] == "track" + assert api._read_status("original")["status"] == "interrupted" + + +def test_active_and_section_revision_jobs_cannot_plain_retry(studio) -> None: + saved_job("active", "running") + saved_job("revision", "error", revisionOf="original") + assert studio.post("/api/jobs/active/retry").status_code == 409 + assert studio.post("/api/jobs/revision/retry").status_code == 409 + assert studio.get("/api/jobs/revision").json()["canRetry"] is False + + +def test_cancelling_queued_job_never_starts_pipeline(studio, monkeypatch: pytest.MonkeyPatch) -> None: + saved_job("queued", "queued") + def forbidden(**kwargs): + pytest.fail("Cancelled job started generation") + monkeypatch.setattr(api, "run_premium_pipeline", forbidden) + assert studio.post("/api/jobs/queued/cancel").json()["status"] == "cancelled" + api._run_job("queued") + assert api._read_status("queued")["status"] == "cancelled" + assert studio.post("/api/jobs/queued/cancel").status_code == 409 + + +def test_running_cancel_is_forwarded_and_cannot_publish_output(studio, monkeypatch: pytest.MonkeyPatch) -> None: + saved_job("working", "queued") + def pipeline(**kwargs): + kwargs["progress"]("mapping", "working") + assert studio.post("/api/jobs/working/cancel").json()["status"] == "cancelling" + assert kwargs["cancel_requested"]() is True + raise api.PipelineCancelled("stopped") + monkeypatch.setattr(api, "run_premium_pipeline", pipeline) + api._run_job("working") + status = studio.get("/api/jobs/working").json() + assert status["status"] == "cancelled" + assert status["localStatus"] is None + assert status["canRetry"] is True + assert status["canCancel"] is False + assert not (api._job_dir("working") / "map.zip").exists() + + +def test_failed_retest_removes_old_pass_until_that_tester_passes_again(studio) -> None: + directory = saved_job("retest", "review_required", localStatus="playtest_candidate", difficulties=["Easy"]) + evidence = {"difficulty": "Easy", "speed": "full", "tester": "mapper", "passed": True} + response = studio.post("/api/jobs/retest/playtests", json=evidence) + assert response.json()["releaseGate"]["allFiveFullSpeed"] is True + evidence["passed"] = False + response = studio.post("/api/jobs/retest/playtests", json=evidence) + assert response.json()["releaseGate"]["allFiveFullSpeed"] is False + assert response.json()["releaseGate"]["unresolvedFailures"] == 1 + evidence["passed"] = True + response = studio.post("/api/jobs/retest/playtests", json=evidence) + assert response.json()["releaseGate"]["allFiveFullSpeed"] is True + assert response.json()["releaseGate"]["unresolvedFailures"] == 0 + assert len(json.loads((directory / "playtests.json").read_text())) == 3 diff --git a/tests/test_learning.py b/tests/test_learning.py new file mode 100644 index 0000000..2eec481 --- /dev/null +++ b/tests/test_learning.py @@ -0,0 +1,211 @@ +"""Human learning data stays local, attributable, bounded, and separate from clearance.""" + +from __future__ import annotations + +import hashlib +import json +import math +import struct +import subprocess +import sys +import wave +from concurrent.futures import ThreadPoolExecutor +from pathlib import Path + +import pytest +from fastapi.testclient import TestClient + +from beatforge import api, learning + + +@pytest.fixture +def studio(tmp_path: Path, monkeypatch: pytest.MonkeyPatch) -> TestClient: + monkeypatch.setattr(api, "JOBS_DIR", tmp_path / "jobs") + monkeypatch.setattr(learning, "LEARNING_FILE", tmp_path / "learning.json") + return TestClient(api.app) + + +def create_map(identifier: str, variant: int = 0, audio_variant: int = 0, **status_fields) -> Path: + api._write_status(identifier, {"status": "review_required", "localStatus": "playtest_candidate", "difficulties": ["Easy", "Hard"], "mappingPlan": {"density": 1.0}, "privateCorpus": "DO-NOT-EXPORT-CORPUS", "inputPath": "C:/private/song.wav", **status_fields}) + folder = api._job_dir(identifier) / "map" + folder.mkdir() + info = {"_version": "2.1.0", "_songName": "Original test score", "_songFilename": "song.wav", "_beatsPerMinute": 120, "_songTimeOffset": 0, "_difficultyBeatmapSets": [{"_beatmapCharacteristicName": "Standard", "_difficultyBeatmaps": []}]} + for difficulty in ("Easy", "Hard"): + name = f"{difficulty}Standard.dat" + chart = {"version": "3.3.0", "colorNotes": [{"b": 1, "x": variant % 4, "y": 0, "c": 0, "d": 1}, {"b": 3, "x": 3, "y": 1, "c": 1, "d": 0}], "bombNotes": [], "obstacles": [], "sliders": [], "burstSliders": []} + (folder / name).write_text(json.dumps(chart)) + info["_difficultyBeatmapSets"][0]["_difficultyBeatmaps"].append({"_difficulty": difficulty, "_beatmapFilename": name, "_noteJumpMovementSpeed": 12, "_noteJumpStartBeatOffset": 0.5}) + (folder / "Info.dat").write_text(json.dumps(info)) + with wave.open(str(folder / "song.wav"), "wb") as audio: + audio.setnchannels(1) + audio.setsampwidth(2) + audio.setframerate(8000) + audio.writeframes(b"".join(struct.pack(" dict: + return {"jobId": job, "difficulty": "Hard", "tester": "local", "ratings": {"flow": 3, "overall": 4}, "tags": ["too_dense"], "notes": "Less dense chorus, please.", **changes} + + +def test_feedback_hashes_actual_chart_and_audio_without_creating_headset_evidence(studio) -> None: + folder = create_map("one") + before_status = api._read_status("one") + before_provenance = (folder / "_beatforge" / "provenance.json").read_bytes() + response = studio.post("/api/feedback", json=feedback(startBeat=2, endBeat=4)) + assert response.status_code == 200 + record = response.json()["feedback"] + assert record["source"] == "human" + assert len(record["chartHash"]) == len(record["mapHash"]) == 64 + assert record["audioHash"] == hashlib.sha256((folder / "song.wav").read_bytes()).hexdigest() + assert record["startBeat"] == 2 + assert record["endBeat"] == 4 + assert api._read_status("one") == before_status + assert (folder / "_beatforge" / "provenance.json").read_bytes() == before_provenance + assert not (api._job_dir("one") / "playtests.json").exists() + summary = studio.get("/api/learning").json() + assert summary["feedbackCount"] == 1 + assert summary["recentFeedback"][0]["id"] == record["id"] + assert summary["modelRegistry"]["automaticTraining"] is False + + +@pytest.mark.parametrize("changes", [ + {"ratings": {"flow": 6}}, {"ratings": {"overall": "5"}}, {"ratings": {"flow": True}}, + {"ratings": {"other": 3}}, {"tags": ["unknown"]}, {"startBeat": 2}, + {"startBeat": 4, "endBeat": 2}, {"startBeat": 2, "endBeat": 1000}, + {"startBeat": "NaN", "endBeat": 4}, {"tester": " "}, + {"source": "human"}, {"chartHash": "forged"}, + {"ratings": {}, "tags": [], "notes": ""}, {"difficulty": "Expert"}, +]) +def test_malformed_feedback_never_writes_learning(studio, changes) -> None: + create_map("one") + response = studio.post("/api/feedback", json=feedback(**changes)) + assert response.status_code == 422 + assert not learning.LEARNING_FILE.exists() + + +def test_feedback_and_presets_survive_a_new_python_process(studio) -> None: + create_map("one") + assert studio.post("/api/feedback", json=feedback()).status_code == 200 + preset = studio.post("/api/presets", json={"name": "My smooth maps", "mappingPlan": {"density": 0.85, "style": "flow"}}) + assert preset.status_code == 200 + assert preset.json()["mappingPlan"]["density"] == 0.85 + script = "import json,sys; from pathlib import Path; from beatforge import learning; learning.LEARNING_FILE=Path(sys.argv[1]); value=learning._read_store(); print(json.dumps({'feedbackCount':len(value['feedback']),'preset':value['presets'][0]}))" + result = subprocess.run([sys.executable, "-c", script, str(learning.LEARNING_FILE)], capture_output=True, text=True, check=True, timeout=30) + recovered = json.loads(result.stdout) + assert recovered["feedbackCount"] == 1 + assert recovered["preset"] == preset.json() + assert studio.get("/api/learning").json()["presets"] == [preset.json()] + + +def test_suggestions_use_distinct_human_charts_and_leave_plan_unchanged(studio) -> None: + for index in range(3): + create_map(f"chart{index}", variant=index) + assert studio.post("/api/feedback", json=feedback(f"chart{index}")).status_code == 200 + plan = {"density": 1.0, "candidateCount": 2} + response = studio.post("/api/learning/suggestions", json={"mappingPlan": plan, "tester": "local", "difficulty": "Hard"}) + assert response.status_code == 200 + result = response.json() + assert result["ready"] is True + assert result["evidenceCount"] == 3 + assert result["suggestedPlan"]["density"] < 1.0 + assert all(api._read_status(f"chart{index}")["mappingPlan"]["density"] == 1.0 for index in range(3)) + other = studio.post("/api/learning/suggestions", json={"mappingPlan": plan, "tester": "someone else"}).json() + assert other["ready"] is False + assert other["evidenceCount"] == 0 + + +def test_comparison_requires_identical_audio_and_different_selected_chart(studio) -> None: + create_map("one", variant=0) + create_map("otheraudio", variant=1, audio_variant=20) + create_map("identical", variant=0) + request = {"preferredJob": "one", "alternateJob": "otheraudio", "difficulty": "Hard", "tester": "local", "notes": "Preferred flow"} + assert studio.post("/api/comparisons", json=request).status_code == 409 + request["alternateJob"] = "identical" + assert studio.post("/api/comparisons", json=request).status_code == 409 + hard = api._job_dir("identical") / "map" / "HardStandard.dat" + hard.write_text(json.dumps(json.loads(hard.read_text()), sort_keys=True, indent=4)) + assert studio.post("/api/comparisons", json=request).status_code == 409 + # Changing a different difficulty does not make Hard a meaningful A/B pair. + easy = api._job_dir("identical") / "map" / "EasyStandard.dat" + chart = json.loads(easy.read_text()) + chart["colorNotes"][0]["x"] = 2 + easy.write_text(json.dumps(chart)) + assert studio.post("/api/comparisons", json=request).status_code == 409 + assert not learning.LEARNING_FILE.exists() + + +def test_latest_comparison_replaces_the_same_testers_vote_in_either_direction(studio) -> None: + create_map("one", variant=0) + create_map("two", variant=1) + request = {"preferredJob": "one", "alternateJob": "two", "difficulty": "Hard", "tester": "local", "notes": "First pass"} + first = studio.post("/api/comparisons", json=request).json() + assert first["comparisonCount"] == 1 + assert first["replaced"] is False + request.update(preferredJob="two", alternateJob="one", notes="Changed my preference") + second = studio.post("/api/comparisons", json=request).json() + assert second["comparisonCount"] == 1 + assert second["replaced"] is True + assert second["comparison"]["id"] == first["comparison"]["id"] + assert second["comparison"]["preferredJob"] == "two" + request["tester"] = "second player" + assert studio.post("/api/comparisons", json=request).json()["comparisonCount"] == 2 + + +def test_revision_acceptance_is_explicit_and_originals_or_failed_revisions_are_rejected(studio) -> None: + create_map("one") + create_map("revision", variant=1, revisionOf="one", revision={"difficulty": "Hard", "startBeat": 2, "endBeat": 4, "seed": 4, "baseHash": "a" * 64}) + before = api._read_status("revision") + assert studio.get("/api/learning").json()["acceptedRevisionCount"] == 0 + assert studio.post("/api/jobs/one/accept-revision", json={"tester": "local"}).status_code == 409 + response = studio.post("/api/jobs/revision/accept-revision", json={"tester": "local", "notes": "Keep this passage"}) + assert response.status_code == 200 + record = response.json()["acceptedRevision"] + assert record["parentJob"] == "one" + assert record["revision"]["startBeat"] == 2 + assert record["source"] == "human" + assert api._read_status("revision") == before + assert not (api._job_dir("revision") / "playtests.json").exists() + qa = api._job_dir("revision") / "map" / "_beatforge" / "qa_report.json" + qa.write_text('{"status":"invalid","errors":["collision"],"warnings":[]}') + assert studio.post("/api/jobs/revision/accept-revision", json={"tester": "local"}).status_code == 409 + + +def test_export_contains_feedback_preferences_and_accepted_metadata_without_source_files(studio) -> None: + create_map("one") + create_map("two", variant=1, revisionOf="one", revision={"difficulty": "Hard", "startBeat": 2, "endBeat": 4}) + studio.post("/api/feedback", json=feedback()) + studio.post("/api/comparisons", json={"preferredJob": "two", "alternateJob": "one", "difficulty": "Hard"}) + studio.post("/api/jobs/two/accept-revision", json={"notes": "Keep it"}) + result = studio.get("/api/learning/export") + assert result.status_code == 200 + exported = result.json() + assert exported["schemaVersion"] == 1 + assert exported["containsAudio"] is False + assert exported["containsOfficialCorpus"] is False + assert len(exported["feedback"]) == len(exported["preferences"]) == len(exported["acceptedRevisions"]) == 1 + for forbidden in ("DO-NOT-EXPORT-CORPUS", "C:/private", "colorNotes", "inputPath", "RIFF"): + assert forbidden not in result.text + + +@pytest.mark.parametrize("broken", ["{broken", '{"schemaVersion":2}', '{"schemaVersion":1,"feedback":"oops","comparisons":[],"presets":[],"acceptedRevisions":[]}']) +def test_unreadable_learning_file_is_preserved(studio, broken) -> None: + learning.LEARNING_FILE.write_text(broken) + assert studio.get("/api/learning").status_code == 503 + assert studio.post("/api/presets", json={"name": "Preset", "mappingPlan": {}}).status_code == 503 + assert learning.LEARNING_FILE.read_text() == broken + + +def test_concurrent_preset_saves_do_not_lose_records(studio) -> None: + def save(index): + return studio.post("/api/presets", json={"name": f"Preset {index}", "mappingPlan": {}}).status_code + with ThreadPoolExecutor(max_workers=5) as pool: + assert list(pool.map(save, range(12))) == [200] * 12 + saved = json.loads(learning.LEARNING_FILE.read_text()) + assert len(saved["presets"]) == 12 + assert len({item["id"] for item in saved["presets"]}) == 12 + assert not list(learning.LEARNING_FILE.parent.glob("learning.*.tmp")) diff --git a/tests/test_mapping_plan.py b/tests/test_mapping_plan.py new file mode 100644 index 0000000..295a446 --- /dev/null +++ b/tests/test_mapping_plan.py @@ -0,0 +1,184 @@ +from __future__ import annotations + +import json +from pathlib import Path +import sys +import threading + +import pytest + +from beatforge.mapping_plan import build_section_plan, normalize_mapping_plan + +SCRIPTS = Path(__file__).resolve().parents[1] / "skills" / "beat-saber-mapping" / "scripts" +sys.path.insert(0, str(SCRIPTS)) +import choreography +from timing_export import ExportClock, project_map_timing, project_plan_timing + + +def analysis_fixture() -> tuple[dict, dict]: + analysis = { + "bpm": 120.0, "durationSeconds": 32.0, + "events": [{"beat": i * 0.5, "strength": (i % 5 + 1) / 5.0, "layer": "mix"} for i in range(4, 128)], + } + sections = {"source": "fixture", "sections": [ + {"label": "verse 1", "startBeat": 0, "endBeat": 32, "intensity": 0.5}, + {"label": "chorus 1", "startBeat": 32, "endBeat": 48, "intensity": 0.8}, + {"label": "chorus 2", "startBeat": 48, "endBeat": 64, "intensity": 0.8}, + ]} + return analysis, sections + + +def test_supported_brief_has_explicit_precedence_and_roundtrips(): + result = normalize_mapping_plan({"brief": "Flowing, sparse verses, bigger chorus, no bombs; follow the bass", "chorusDensity": 1.1}) + assert result["noBombs"] is True + assert result["verseDensity"] == 0.75 + assert result["chorusDensity"] == 1.1 + assert result["style"] == "flow" + assert result["dominantInstrument"] == "bass" + assert normalize_mapping_plan(json.loads(json.dumps(result))) == result + + +@pytest.mark.parametrize("value", [ + {"density": 2}, {"density": True}, {"density": float("nan")}, + {"noBombs": "false"}, {"candidateCount": 0}, {"schemaVersion": 2}, + {"dominantInstrument": "rain"}, {"style": "unsafe"}, {"typo": 1}, + {"sectionOverrides": [{"id": "x"}, {"id": "x"}]}, +]) +def test_invalid_controls_are_rejected(value): + with pytest.raises(ValueError): + normalize_mapping_plan(value) + + +def test_section_override_preserves_other_sections_and_repeated_motif(): + analysis, sections = analysis_fixture() + initial = build_section_plan(analysis, sections, {}) + changed = build_section_plan(analysis, sections, {"sectionOverrides": [{"id": "section-001", "density": 0.5}]}) + assert changed["sections"][0] == initial["sections"][0] + assert changed["sections"][2] == initial["sections"][2] + assert changed["sections"][1]["density"] == 0.5 + assert changed["sections"][1]["motifId"] == changed["sections"][2]["motifId"] == "chorus" + with pytest.raises(ValueError, match="unknown section"): + build_section_plan(analysis, sections, {"sectionOverrides": [{"id": "missing"}]}) + + +def test_density_changes_real_intent_selection_without_changing_recovery_limits(): + analysis, sections = analysis_fixture() + original_recovery = choreography.CONFIGS["Expert"].recovery_beats + sparse = choreography.select_intents(analysis, build_section_plan(analysis, sections, {"density": 0.5}), "Expert") + dense = choreography.select_intents(analysis, build_section_plan(analysis, sections, {"density": 1.5}), "Expert") + assert len(sparse) < len(dense) + assert choreography.CONFIGS["Expert"].recovery_beats == original_recovery + assert all(right["beat"] - left["beat"] >= choreography.CONFIGS["Expert"].peak_min_gap for left, right in zip(dense, dense[1:])) + + +def test_dominant_instrument_selects_its_actual_stem_accents(): + analysis, sections = analysis_fixture() + for index, event in enumerate(analysis["events"]): + event["stemEnergy"] = {"bass": 1.0 if index % 2 else 0.0, "drums": 0.0 if index % 2 else 1.0} + event["strength"] = 0.5 + bass = choreography.select_intents(analysis, build_section_plan(analysis, sections, {"dominantInstrument": "bass"}), "Normal") + drums = choreography.select_intents(analysis, build_section_plan(analysis, sections, {"dominantInstrument": "drums"}), "Normal") + assert [item["beat"] for item in bass] != [item["beat"] for item in drums] + + +def test_repeated_chorus_reuses_local_pose_preferences(): + analysis, sections = analysis_fixture() + # Identical sound at the same local beat of two chorus instances. + analysis["events"] = [{"beat": beat, "strength": 3.0} for beat in (34, 50)] + planned = build_section_plan(analysis, sections, {"density": 1.5}) + intents = [item for item in choreography.select_intents(analysis, planned, "Hard") if item["beat"] in (34, 50)] + assert len(intents) == 2 + domains = [choreography._pose_domain(intent, 0, 0, index, 120, choreography.CONFIGS["Hard"].recovery_beats) for index, intent in enumerate(intents)] + shapes = [[(pose["head"]["x"], pose["head"]["y"], pose["head"]["d"]) for pose in domain] for domain in domains] + assert shapes[0] == shapes[1] + + +def simple_map(notes): + return {"version": "3.3.0", "colorNotes": notes, "bombNotes": [], "obstacles": [], "sliders": [], "burstSliders": []} + + +def test_high_corpus_score_cannot_select_an_unsafe_candidate(): + good = simple_map([{"b": 4, "x": 0, "y": 0, "c": 0, "d": 1, "a": 0}]) + bad = simple_map([ + {"b": 4, "x": 0, "y": 1, "c": 0, "d": 5, "a": 0}, + {"b": 4, "x": 2, "y": 0, "c": 1, "d": 4, "a": 0}, + ]) + safe_rank = choreography.rank_choreography_candidate(good, "Expert", 120, {"score": 0}, {}, 1) + unsafe_rank = choreography.rank_choreography_candidate(bad, "Expert", 120, {"score": 100}, {}, 1) + assert safe_rank["eligible"] + assert not unsafe_rank["eligible"] + assert choreography.select_ranked_candidate([{"index": 0, **unsafe_rank}, {"index": 1, **safe_rank}])["index"] == 1 + with pytest.raises(ValueError, match="safety gates"): + choreography.select_ranked_candidate([{"index": 0, **unsafe_rank}]) + + +def test_corpus_ranking_changes_the_actual_winner(): + chart = simple_map([{"b": 4, "x": 0, "y": 0, "c": 0, "d": 1, "a": 0}]) + candidates = [{"index": i, **choreography.rank_choreography_candidate(chart, "Expert", 120, {"score": score}, {}, 1)} for i, score in enumerate((20, 90))] + assert choreography.select_ranked_candidate(candidates)["index"] == 1 + + +def test_premium_plan_generates_a_valid_accessible_chart(tmp_path): + analysis, sections = analysis_fixture() + # Keep the real solver fixture small; the unit rank test covers multiple candidates. + analysis["durationSeconds"] = 8.0 + analysis["events"] = [event for event in analysis["events"] if event["beat"] < 16] + sections["sections"] = [{"label": "verse", "startBeat": 0, "endBeat": 16, "intensity": 0.5}] + maps, report = choreography.generate_all(analysis, sections, 123, tmp_path / "missing.sqlite", ("Easy",), mapping_plan={"candidateCount": 1, "noBombs": True, "noWalls": True}) + chart = maps["Easy"] + assert chart["colorNotes"] + assert chart["bombNotes"] == chart["obstacles"] == [] + assert report["candidateRanking"]["Easy"]["candidates"][0]["eligible"] + assert report["mappingPlan"]["sections"][0]["id"] == "section-000" + + +def test_premium_cancellation_stops_a_real_spawned_mapper(tmp_path, monkeypatch): + from beatforge import premium + script = tmp_path / "generate_map.py" + script.write_text("import sys, time\nprint('BEATFORGE_PROGRESS\\trunning\\tready', file=sys.stderr, flush=True)\ntime.sleep(30)\n", encoding="utf-8") + monkeypatch.setattr(premium, "SCRIPTS", tmp_path) + cancelled = threading.Event() + def progress(stage, detail, **kwargs): + if stage == "running": + cancelled.set() + with pytest.raises(premium.PipelineCancelled): + premium.run_premium_pipeline(audio=tmp_path / "song.wav", output=tmp_path / "out", title="Test", artist="Test", mapper="Test", seed=1, anchors=None, palette=None, progress=progress, cancel_requested=cancelled.is_set) + + +def test_export_uses_actual_offset_and_variable_tempo_for_notes_holds_and_lights(): + # Beat zero is 0.25 seconds into the song. The second bar doubles in duration. + analysis = {"bpm": 120, "sampleRate": 44100, "beatGrid": [ + {"beat": 0, "sample": 11025}, {"beat": 4, "sample": 99225}, + {"beat": 8, "sample": 275625}, + ]} + chart = simple_map([{"b": 6, "x": 0, "y": 0, "c": 0, "d": 1, "a": 0}]) + chart["sliders"] = [{"b": 3, "tb": 5}] + chart["burstSliders"] = [{"b": 4, "tb": 5}] + chart["obstacles"] = [{"b": 3, "d": 2}] + chart["basicBeatmapEvents"] = [{"b": 6, "et": 1, "i": 5, "f": 1.0}] + chart["lightColorEventBoxGroups"] = [{"b": 4, "g": 0, "e": [{"f": {"b": 1}, "e": [{"b": 1, "c": 0}]}]}] + exported = project_map_timing(chart, analysis) + assert exported["colorNotes"][0]["b"] == 8.5 + assert exported["sliders"][0] == {"b": 3.5, "tb": 6.5} + assert exported["burstSliders"][0] == {"b": 4.5, "tb": 6.5} + assert exported["obstacles"][0] == {"b": 3.5, "d": 3.0} + assert exported["basicBeatmapEvents"][0]["b"] == 8.5 + box = exported["lightColorEventBoxGroups"][0] + assert box["b"] == 4.5 + assert box["e"][0]["e"][0]["b"] == 2 + assert box["e"][0]["f"]["b"] == 1 + assert chart["colorNotes"][0]["b"] == 6 # authored input was preserved + with pytest.raises(ValueError, match="already been projected"): + project_map_timing(exported, analysis) + + +def test_exported_section_boundaries_match_notes_for_preview_and_revision(): + analysis = {"bpm": 120, "sampleRate": 44100, "beatGrid": [{"beat": 0, "sample": 11025}, {"beat": 8, "sample": 187425}]} + plan = {"sections": [{"id": "section-001", "startBeat": 2, "endBeat": 4}]} + projected = project_plan_timing(plan, analysis) + section = projected["sections"][0] + assert section["startBeat"] == ExportClock(analysis).beat(2) + assert section["endBeat"] == ExportClock(analysis).beat(4) + assert section["startSeconds"] == 1.25 + assert section["authoredStartBeat"] == 2 + assert plan["sections"][0]["startBeat"] == 2 diff --git a/tests/test_preview.py b/tests/test_preview.py new file mode 100644 index 0000000..e1bc781 --- /dev/null +++ b/tests/test_preview.py @@ -0,0 +1,335 @@ +"""Preview decoding and isolated revision contracts using actual audio and map files.""" + +from __future__ import annotations + +import copy +import hashlib +import json +from pathlib import Path +import sys +from zipfile import ZipFile + +from fastapi import HTTPException +from fastapi.testclient import TestClient +import numpy as np +import pytest +import soundfile as sf + +from beatforge import api, preview + +ROOT = Path(__file__).resolve().parents[1] +SCRIPTS = ROOT / "skills" / "beat-saber-mapping" / "scripts" +if str(SCRIPTS) not in sys.path: + sys.path.insert(0, str(SCRIPTS)) +import choreography +from validate_map import validate_package + + +def write_json(path: Path, payload) -> None: + path.parent.mkdir(parents=True, exist_ok=True) + path.write_text(json.dumps(payload, indent=2) + "\n", encoding="utf-8") + + +def note(beat: float, direction: int = 1, x: int = 0, color: int = 0) -> dict: + return {"b": beat, "x": x, "y": 0 if direction == 1 else 1, "c": color, "d": direction, "a": 0} + + +def chart(notes: list[dict]) -> dict: + return {"version": "3.3.0", "colorNotes": notes, "bombNotes": [], "obstacles": [], "sliders": [], "burstSliders": [], "basicBeatmapEvents": [], "bpmEvents": [], "customData": {"untouched": "source metadata"}} + + +@pytest.fixture +def studio(tmp_path: Path, monkeypatch: pytest.MonkeyPatch): + monkeypatch.setattr(api, "JOBS_DIR", tmp_path / "jobs") + monkeypatch.setattr(api, "find_custom_levels", lambda: None) + def forbidden_install(*args, **kwargs): + raise AssertionError("Preview and revision tests must never install a map") + monkeypatch.setattr(api, "_install_pack_to_custom_levels", forbidden_install) + return TestClient(api.app) + + +@pytest.fixture +def saved_map(studio) -> Path: + folder = api._job_dir("original") / "map" + folder.mkdir(parents=True) + with ZipFile(ROOT / "web" / "assets" / "demo" / "map.zip") as archive: + info = json.loads(archive.read("Info.dat")) + (folder / "cover.png").write_bytes(archive.read("cover.png")) + info["_songName"] = "Revision fixture" + info["_songFilename"] = "song.ogg" + info["_difficultyBeatmapSets"][0]["_difficultyBeatmaps"] = [entry for entry in info["_difficultyBeatmapSets"][0]["_difficultyBeatmaps"] if entry["_difficulty"] in ("Easy", "Hard")] + write_json(folder / "Info.dat", info) + for difficulty in ("Easy", "Hard"): + notes = [note(beat, 1 if index % 2 == 0 else 0) for index, beat in enumerate((2, 4, 6, 8, 10, 12))] + if difficulty == "Hard": + notes.append(note(14, 1)) + write_json(folder / f"{difficulty}Standard.dat", chart(notes)) + samples = np.zeros((44100 * 8, 2), dtype=np.float32) + # Distinct stereo pulses prove the preview decodes samples, rather than fake bars. + samples[11025:11525, 0] = 0.25 + samples[22050:22550, 1] = -0.75 + sf.write(folder / "song.ogg", samples, 44100, format="OGG", subtype="VORBIS") + analysis = { + "schemaVersion": 1, "status": "timing_verified", "bpm": 120, "sampleRate": 44100, + "durationSeconds": 8, "durationSamples": len(samples), + "beatGrid": [{"beat": index / 2, "sample": index * 11025, "timeSeconds": index / 4} for index in range(32)], + "events": [{"beat": index / 2, "strength": 3.0, "layer": "mix"} for index in range(4, 31)], + "clickTrackEvidence": {"checkpoints": [{"sample": value, "label": label} for value, label in ((0, "start"), (176400, "middle"), (330750, "end"))]}, + } + write_json(folder / "_beatforge" / "analysis.json", analysis) + write_json(folder / "_beatforge" / "sections.json", {"sections": [{"id": "section-000", "label": "verse", "startBeat": 0, "endBeat": 8, "intensity": 0.5}, {"id": "section-001", "label": "chorus", "startBeat": 8, "endBeat": 16, "intensity": 0.8}]}) + write_json(folder / "_beatforge" / "provenance.json", {"seed": 12, "releaseGate": {"vrPlaytest": True}, "humanPlaytests": [{"passed": True}], "reviews": [{"approved": True}], "vrPlaytestPassed": True}) + qa = validate_package(folder) + assert not qa.errors, qa.to_dict() + write_json(folder / "_beatforge" / "qa_report.json", qa.to_dict()) + api._write_status("original", {"id": "original", "status": "review_required", "localStatus": "playtest_candidate", "title": "Revision fixture", "artist": "Fixture", "difficulties": ["Easy", "Hard"], "installed": False}) + return folder + + +def revision_payload(folder: Path, **changes) -> dict: + return {"difficulty": "Easy", "startBeat": 6, "endBeat": 8, "baseHash": preview.chart_identity(folder), "seed": 123, "mappingPlan": {"candidateCount": 1, "noWalls": True, "noBombs": True}, **changes} + + +@pytest.mark.parametrize("relative", ["../outside.dat", "missing.dat"]) +def test_confined_rejects_escape_and_missing_files(tmp_path, relative): + root = tmp_path / "map" + root.mkdir() + (tmp_path / "outside.dat").write_text("private") + with pytest.raises(HTTPException) as raised: + preview.confined(root, relative) + assert raised.value.status_code == 404 + + +def test_confined_rejects_absolute_external_path_and_symlink(tmp_path): + root = tmp_path / "map" + root.mkdir() + outside = tmp_path / "outside.dat" + outside.write_text("private") + with pytest.raises(HTTPException): + preview.confined(root, str(outside.resolve())) + try: + (root / "linked.dat").symlink_to(outside) + except OSError: + pytest.skip("This Windows account cannot create symlinks") + with pytest.raises(HTTPException): + preview.confined(root, "linked.dat") + + +def test_waveform_reads_real_stereo_amplitudes_in_bounded_bins(tmp_path): + path = tmp_path / "pulses.wav" + samples = np.zeros((800, 2), dtype=np.float32) + samples[105, 0] = 0.25 + samples[505, 1] = -0.75 + sf.write(path, samples, 800, subtype="FLOAT") + duration, peaks = preview.waveform(path, bins=8) + assert duration == 1.0 + assert peaks == [0.0, 0.25, 0.0, 0.0, 0.0, 0.75, 0.0, 0.0] + + +def test_preview_selects_actual_difficulty_and_filters_qa(studio, saved_map): + qa_path = saved_map / "_beatforge" / "qa_report.json" + qa = preview.read_json(qa_path) + qa["warnings"] = [{"file": "EasyStandard.dat", "code": "EASY_ONLY"}, {"file": "HardStandard.dat", "code": "HARD_ONLY"}, {"file": None, "code": "PACKAGE"}] + write_json(qa_path, qa) + response = studio.get("/api/jobs/original/preview?difficulty=Hard") + assert response.status_code == 200 + payload = response.json() + assert payload["difficulty"] == "Hard" + assert len(payload["chart"]["colorNotes"]) == 7 + assert {item["code"] for item in payload["findings"]} == {"HARD_ONLY", "PACKAGE"} + assert payload["difficulties"] == ["Easy", "Hard"] + assert payload["duration"] == 8 + assert max(payload["waveform"]) > 0.5 + assert payload["audioUrl"] == "/api/jobs/original/audio" + assert studio.get("/api/jobs/original/preview?difficulty=ExpertPlus").status_code == 404 + assert studio.get("/api/jobs/original/audio").content == (saved_map / "song.ogg").read_bytes() + + +def test_preview_rejects_info_audio_path_escape(studio, saved_map): + outside = saved_map.parent / "private.ogg" + outside.write_bytes(b"private") + info = preview.read_json(saved_map / "Info.dat") + info["_songFilename"] = "../private.ogg" + write_json(saved_map / "Info.dat", info) + assert studio.get("/api/jobs/original/preview").status_code == 404 + assert studio.get("/api/jobs/original/audio").status_code == 404 + + +def test_waveform_cache_invalidates_when_audio_changes(saved_map): + first = preview.preview_payload(saved_map) + sf.write(saved_map / "song.ogg", np.zeros(44100, dtype=np.float32), 44100, format="OGG", subtype="VORBIS") + second = preview.preview_payload(saved_map) + assert first["duration"] == 8 and second["duration"] == 1 + assert max(second["waveform"]) == 0 + + +def test_corrupt_matching_hash_waveform_cache_is_recomputed(saved_map): + audio_hash = hashlib.sha256((saved_map / "song.ogg").read_bytes()).hexdigest() + write_json(saved_map / "_beatforge" / "preview-waveform.json", {"audioHash": audio_hash, "duration": 8, "waveform": [float("nan")]}) + payload = preview.preview_payload(saved_map) + assert payload["duration"] == 8 + assert len(payload["waveform"]) > 1 + assert all(np.isfinite(value) for value in payload["waveform"]) + assert max(payload["waveform"]) > 0.5 + + +def test_stale_revision_hash_does_not_create_a_child(studio, saved_map): + request = revision_payload(saved_map) + existing = preview.read_json(saved_map / "EasyStandard.dat") + existing["customData"]["changedAfterPreview"] = True + write_json(saved_map / "EasyStandard.dat", existing) + response = studio.post("/api/jobs/original/revise", json=request) + assert response.status_code == 409 + assert [folder.name for folder in api.JOBS_DIR.iterdir()] == ["original"] + + +def test_audio_change_invalidates_revision_base_hash(studio, saved_map): + request = revision_payload(saved_map) + sf.write(saved_map / "song.ogg", np.zeros(44100, dtype=np.float32), 44100, format="OGG", subtype="VORBIS") + response = studio.post("/api/jobs/original/revise", json=request) + assert response.status_code == 409 + assert [folder.name for folder in api.JOBS_DIR.iterdir()] == ["original"] + + +def test_half_open_merge_preserves_every_outside_object_and_custom_metadata(): + original = chart([note(1), note(4), note(5), note(8), note(10)]) + original["obstacles"] = [{"b": 0, "d": 1, "x": 0, "w": 1, "h": 3}, {"b": 5, "d": 1, "x": 3, "w": 1, "h": 3}, {"b": 9, "d": 1, "x": 0, "w": 1, "h": 3}] + original["sliders"] = [{"b": 1, "tb": 2}, {"b": 5, "tb": 6}, {"b": 9, "tb": 10}] + original["basicBeatmapEvents"] = [{"b": 1, "i": 1}, {"b": 4, "i": 2}, {"b": 8, "i": 3}] + candidate = chart([note(0, x=3), note(4, x=1), note(7, x=1), note(8, x=3)]) + candidate["obstacles"] = [{"b": 6, "d": 1, "x": 2, "w": 1, "h": 3}] + candidate["sliders"] = [{"b": 5, "tb": 7}] + candidate["basicBeatmapEvents"] = [{"b": 4, "i": 7}, {"b": 8, "i": 9}] + before = copy.deepcopy(original) + merged = preview.merge_section(original, candidate, 4, 8) + assert original == before + for collection in preview.COLLECTIONS: + expected = [item for item in before.get(collection, []) if item["b"] < 4 or item["b"] >= 8] + actual = [item for item in merged.get(collection, []) if item["b"] < 4 or item["b"] >= 8] + assert actual == expected + assert merged["customData"] == original["customData"] + assert [item["x"] for item in merged["colorNotes"] if 4 <= item["b"] < 8] == [1, 1] + assert next(item for item in merged["colorNotes"] if item["b"] == 8)["x"] == 0 + + +@pytest.mark.parametrize("collection,object_", [ + ("sliders", {"b": 3, "tb": 5}), ("burstSliders", {"b": 6, "tb": 9}), + ("obstacles", {"b": 3, "d": 2}), ("obstacles", {"b": 7, "d": 2}), +]) +@pytest.mark.parametrize("on_candidate", [False, True]) +def test_crossing_holds_and_walls_are_rejected_on_either_side(collection, object_, on_candidate): + original, candidate = chart([]), chart([]) + (candidate if on_candidate else original)[collection] = [object_] + with pytest.raises(ValueError, match="cuts through"): + preview.merge_section(original, candidate, 4, 8) + + +def test_crossing_nested_light_event_is_rejected(): + original = chart([]) + original["lightColorEventBoxGroups"] = [{"b": 7, "g": 1, "e": [{"e": [{"b": 2, "c": 0}]}]}] + with pytest.raises(ValueError, match="cuts through|timed|light"): + preview.merge_section(original, chart([]), 4, 8) + + +def test_existing_crossing_hold_refuses_revision_before_copy(studio, saved_map): + path = saved_map / "EasyStandard.dat" + original = preview.read_json(path) + original["sliders"] = [{"b": 5, "tb": 7}] + write_json(path, original) + response = studio.post("/api/jobs/original/revise", json=revision_payload(saved_map)) + assert response.status_code == 422 + assert [folder.name for folder in api.JOBS_DIR.iterdir()] == ["original"] + + +def test_source_bpm_events_require_explicit_conversion_before_revision(studio, saved_map): + path = saved_map / "EasyStandard.dat" + original = preview.read_json(path) + original["bpmEvents"] = [{"b": 8, "m": 60}] + write_json(path, original) + response = studio.post("/api/jobs/original/revise", json=revision_payload(saved_map)) + assert response.status_code in (409, 422) + assert [folder.name for folder in api.JOBS_DIR.iterdir()] == ["original"] + + +def test_revision_range_cannot_extend_beyond_audio_duration(studio, saved_map): + before = (saved_map / "EasyStandard.dat").read_bytes() + response = studio.post("/api/jobs/original/revise", json=revision_payload(saved_map, startBeat=0, endBeat=16.01)) + assert response.status_code == 422 + assert "beyond the song" in response.json()["detail"] + assert [folder.name for folder in api.JOBS_DIR.iterdir()] == ["original"] + assert (saved_map / "EasyStandard.dat").read_bytes() == before + + +def test_revision_creates_child_preserves_original_and_other_difficulty(studio, saved_map, monkeypatch): + before = {path.relative_to(saved_map).as_posix(): path.read_bytes() for path in saved_map.rglob("*") if path.is_file()} + replacement = chart([note(6, 1, x=1)]) + monkeypatch.setattr(choreography, "generate_all", lambda *args, **kwargs: ({"Easy": replacement}, {"fixture": "safe section"})) + response = studio.post("/api/jobs/original/revise", json=revision_payload(saved_map)) + assert response.status_code == 200 + child_id = response.json()["id"] + assert child_id != "original" + status = api._read_status(child_id) + assert status["revisionOf"] == "original" + assert status["localStatus"] == "playtest_candidate" + assert status["installed"] is False + child = api._job_dir(child_id) / "map" + for relative, data in before.items(): + assert (saved_map / relative).read_bytes() == data + assert (child / "HardStandard.dat").read_bytes() == before["HardStandard.dat"] + original_chart = json.loads(before["EasyStandard.dat"]) + revised_chart = preview.read_json(child / "EasyStandard.dat") + assert [n for n in revised_chart["colorNotes"] if n["b"] < 6 or n["b"] >= 8] == [n for n in original_chart["colorNotes"] if n["b"] < 6 or n["b"] >= 8] + assert next(n for n in revised_chart["colorNotes"] if n["b"] == 6)["x"] == 1 + provenance = preview.read_json(child / "_beatforge" / "provenance.json") + assert not provenance.get("humanPlaytests") and not provenance.get("reviews") and not provenance.get("vrPlaytestPassed") + assert (api._job_dir(child_id) / "map.zip").is_file() + assert studio.get(f"/api/jobs/{child_id}/download").status_code == 200 + + +def test_boundary_failure_keeps_invalid_child_uninstalled_and_undownloadable(studio, saved_map, monkeypatch): + before = (saved_map / "EasyStandard.dat").read_bytes() + # The new note is safe alone but cannot follow the retained note at beat 6. + replacement = chart([note(6.1, 1)]) + monkeypatch.setattr(choreography, "generate_all", lambda *args, **kwargs: ({"Easy": replacement}, {})) + response = studio.post("/api/jobs/original/revise", json=revision_payload(saved_map, startBeat=6.05)) + assert response.status_code == 200 + child_id = response.json()["id"] + status = api._read_status(child_id) + assert status["status"] == "invalid" and status["localStatus"] == "invalid" + assert status["qa"]["errors"] > 0 + assert status["installed"] is False + assert (saved_map / "EasyStandard.dat").read_bytes() == before + assert not (api._job_dir(child_id) / "map.zip").exists() + assert studio.get(f"/api/jobs/{child_id}/download").status_code == 409 + assert studio.post(f"/api/jobs/{child_id}/install").status_code == 409 + + +def test_real_generator_projection_and_complete_validation_in_revision(studio, saved_map, monkeypatch, tmp_path): + from timing_export import project_map_timing + analysis_path = saved_map / "_beatforge" / "analysis.json" + analysis = preview.read_json(analysis_path) + # A quarter-second origin catches a missing authored-to-audio projection. + for row in analysis["beatGrid"]: + row["sample"] += 11025 + row["timeSeconds"] += 0.25 + analysis["beatGrid"] = [row for row in analysis["beatGrid"] if row["sample"] < analysis["durationSamples"]] + write_json(analysis_path, analysis) + for name in ("EasyStandard.dat", "HardStandard.dat"): + write_json(saved_map / name, project_map_timing(preview.read_json(saved_map / name), analysis)) + real_generate = choreography.generate_all + def isolated_generate(analysis, sections, seed, **kwargs): + return real_generate(analysis, sections, seed, corpus_database=tmp_path / "no-corpus.sqlite", **kwargs) + monkeypatch.setattr(choreography, "generate_all", isolated_generate) + before = (saved_map / "EasyStandard.dat").read_bytes() + response = studio.post("/api/jobs/original/revise", json=revision_payload(saved_map, startBeat=0, endBeat=16)) + assert response.status_code == 200 + child_id = response.json()["id"] + status = api._read_status(child_id) + assert status.get("localStatus") == "playtest_candidate", status + child = api._job_dir(child_id) / "map" + assert not validate_package(child).errors + assert (saved_map / "EasyStandard.dat").read_bytes() == before + notes = preview.read_json(child / "EasyStandard.dat")["colorNotes"] + assert notes + assert all(abs((item["b"] % 1.0) - 0.5) < 1e-6 for item in notes) + assert (api._job_dir(child_id) / "map.zip").is_file() diff --git a/tests/test_studio_experience.py b/tests/test_studio_experience.py new file mode 100644 index 0000000..520d224 --- /dev/null +++ b/tests/test_studio_experience.py @@ -0,0 +1,248 @@ +"""Browser acceptance of real sample playback and isolated local-workflow contracts.""" + +from __future__ import annotations + +import copy +import json +from email.parser import BytesParser +from email.policy import default +from pathlib import Path +from urllib.parse import urlparse + +import pytest + +from test_browser import _playwright_chromium, studio_url + +ROOT = Path(__file__).resolve().parents[1] +DEMO = json.loads((ROOT / "web" / "assets" / "demo" / "preview.json").read_text()) +A = "aaaaaaaaaaaa" +B = "bbbbbbbbbbbb" +C = "cccccccccccc" + + +@pytest.fixture +def browser_page(studio_url): + playwright, browser = _playwright_chromium() + context = browser.new_context(viewport={"width": 1280, "height": 1000}) + page = context.new_page() + errors = [] + page.on("pageerror", lambda error: errors.append(str(error))) + yield page, studio_url + context.close() + browser.close() + playwright.stop() + assert errors == [], errors + + +def job(identifier=A, state="review_required", **extra): + return {"id": identifier, "title": "Saved score", "artist": "Fixture", "status": state, "difficulties": ["Hard"], "localStatus": "playtest_candidate" if state == "review_required" else None, "canRetry": state in {"review_required", "interrupted", "cancelled"}, "canCancel": state in {"running", "queued"}, "mappingPlan": {"density": 0.75, "style": "flow"}, "stages": [], "releaseGate": {"evidenceCount": 0, "localHardGates": True, "aiReleaseRoute": False}, **extra} + + +def preview(identifier=A, notes="Hard"): + result = {key: copy.deepcopy(value) for key, value in DEMO.items() if key not in {"charts", "summary"}} + result.update(jobId=identifier, difficulty="Hard", chart=copy.deepcopy(DEMO["charts"][notes]), difficulties=["Hard"], chartHash=identifier[0] * 64, audioHash="f" * 64, audioUrl="/assets/demo/song.ogg", canRevise=True) + return result + + +def mock_api(page, jobs=None, previews=None, suggestions=None, feedback_error=None): + """Mock service state only; sample bytes and all UI code are served unchanged.""" + jobs = jobs if jobs is not None else {} + previews = previews if previews is not None else {} + calls = [] + suggestions = suggestions or {"ready": False, "evidenceCount": 0, "reasons": ["Rate three distinct charts."], "changedFields": {}, "suggestedPlan": {"density": 1}} + + def respond(route, payload, status=200): + route.fulfill(status=status, content_type="application/json", body=json.dumps(payload)) + + def handler(route): + request = route.request + path = urlparse(request.url).path + body = request.post_data_json if request.method == "POST" and "application/json" in request.headers.get("content-type", "") else None + calls.append((request.method, path, body)) + if path == "/api/jobs": + return respond(route, {"jobs": list(jobs.values()), "total": len(jobs)}) + if path == "/api/learning": + return respond(route, {"feedbackCount": 0, "comparisonCount": 0, "presets": [], "recentFeedback": [], "suggestions": suggestions}) + if path == "/api/learning/suggestions": + return respond(route, suggestions) + if path == "/api/mapping-plan/resolve": + return respond(route, body or {}) + if path == "/api/beat-saber/status": + return respond(route, {"found": False, "path": None}) + if path == "/api/beat-saber/playtest-maps": + return respond(route, {"found": False, "maps": []}) + if path.startswith("/api/settings/providers/"): + return respond(route, {"configured": False, "runnerInstalled": False, "label": "OpenAI Codex", "model": "test-model", "maximumCostUsd": 5}) + if path == "/api/feedback": + return respond(route, {"detail": feedback_error}, 409) if feedback_error else respond(route, {"feedback": body, "feedbackCount": 1}) + if path == "/api/comparisons": + return respond(route, {"comparison": body, "comparisonCount": 1}) + if path.startswith("/api/jobs/"): + parts = path.split("/") + identifier = parts[3] + action = parts[4] if len(parts) > 4 else "" + if action == "preview" and identifier in previews: + return respond(route, previews[identifier]) + if identifier not in jobs: + return respond(route, {"detail": "Run not found"}, 404) + if action == "events": + return route.fulfill(status=200, content_type="text/event-stream", body=f"event: progress\ndata: {json.dumps(jobs[identifier])}\n\nevent: end\ndata: {{}}\n\n") + if action == "retry": + jobs[C] = job(C, "needs_anchors", retryOf=identifier) + return respond(route, {"id": C, "status": "queued", "retryOf": identifier}) + if action == "cancel": + jobs[identifier].update(status="cancelled", canCancel=False, canRetry=True) + return respond(route, {"id": identifier, "status": "cancelled"}) + if not action: + return respond(route, jobs[identifier]) + return respond(route, {"detail": f"Unexpected test request {path}"}, 404) + + page.route("**/api/**", handler) + return calls + + +def test_sample_plays_actual_audio_and_switches_actual_difficulties(browser_page): + page, url = browser_page + calls = mock_api(page) + page.goto(url, wait_until="networkidle") + page.locator("#loadDemo").click() + page.wait_for_function("window.BeatForgePreview.getState()?.notes > 0 && document.querySelector('#previewAudio').duration === 30") + assert page.evaluate("window.BeatForgePreview.getState().notes") == len(DEMO["charts"]["Hard"]["colorNotes"]) + assert page.locator("#previewDifficulty option").all_text_contents() == DEMO["difficulties"] + page.locator("#previewPlay").click() + page.wait_for_function("document.querySelector('#previewAudio').currentTime > 0.15") + page.locator("#previewPlay").click() + assert page.evaluate("document.querySelector('#previewAudio').paused") is True + page.locator("#previewSeek").fill("12") + page.locator("#previewSeek").dispatch_event("input") + page.wait_for_function("Number(document.querySelector('#chartCanvas').dataset.renderedNotes) > 0") + assert float(page.locator("#chartCanvas").get_attribute("data-time")) == pytest.approx(12, abs=0.1) + page.locator("#previewDifficulty").select_option("Easy") + assert page.evaluate("window.BeatForgePreview.getState().notes") == len(DEMO["charts"]["Easy"]["colorNotes"]) + assert page.evaluate("window.BeatForgePreview.getState().time") == pytest.approx(12, abs=0.1) + page.locator("#generate").click() + page.wait_for_function("window.BeatForgeApp.getStudioState().job?.demo === true") + assert "20 notes" in page.locator("#difficultyList").inner_text() + assert page.locator("#download").get_attribute("href").endswith("assets/demo/map.zip") + assert page.evaluate("window.BeatForgeApp.getStudioState().job.releaseGate.evidenceCount") == 0 + assert not any(method == "POST" and path == "/api/generate" for method, path, _ in calls) + + +def test_creative_controls_are_sent_with_the_real_upload(browser_page, monkeypatch): + from beatforge import api + + page, url = browser_page + monkeypatch.setattr(api, "_run_job", lambda identifier: None) + jobs = {A: job(A, "needs_anchors")} + mock_api(page, jobs=jobs) + captured = {} + def capture(route): + request = route.request + message = BytesParser(policy=default).parsebytes(b"Content-Type: " + request.headers["content-type"].encode() + b"\r\nMIME-Version: 1.0\r\n\r\n" + request.post_data_buffer) + for part in message.iter_parts(): + name = part.get_param("name", header="content-disposition") + captured[name] = part.get_payload(decode=True) + route.continue_() + page.route("**/api/generate", capture) + page.goto(url, wait_until="networkidle") + page.locator("#audioFile").set_input_files(ROOT / "web" / "assets" / "demo" / "song.ogg") + page.locator("#creativeBrief").fill("Flowing patterns with no bombs") + page.locator("#planDensity").select_option("0.75") + page.locator("#planInstrument").select_option("drums") + page.locator("#planBombs").select_option("hide") + page.locator('.diff-toggle[data-difficulty="Easy"]').click() + with page.expect_response(lambda response: urlparse(response.url).path == "/api/generate") as generated: + page.locator("#generate").click() + response = generated.value + assert response.status == 200 + identifier = response.json()["id"] + assert (api._job_dir(identifier) / "input.ogg").read_bytes() == (ROOT / "web" / "assets" / "demo" / "song.ogg").read_bytes() + plan = json.loads(captured["mappingPlan"]) + assert plan["density"] == 0.75 + assert plan["dominantInstrument"] == "drums" + assert plan["noBombs"] is True + assert plan["brief"] == "Flowing patterns with no bombs" + assert captured["difficulties"].decode() == "Normal,Hard,Expert,ExpertPlus" + assert captured["engine"] == b"premium" + + +def test_static_page_blocks_full_generation_and_explains_local_studio(browser_page): + page, url = browser_page + page.context.add_init_script("window.BEATFORGE_STATIC = true") + calls = mock_api(page) + page.goto(url, wait_until="networkidle") + page.locator("#audioFile").set_input_files(ROOT / "web" / "assets" / "demo" / "song.ogg") + page.locator("#generate").click() + assert page.locator("#statusTitle").inner_text() == "Open your local Studio to generate" + assert "Start-BeatForge.ps1" in page.locator("#statusDetail").inner_text() + assert page.locator("#feedbackForm").is_hidden() + assert not any(method == "POST" and path == "/api/generate" for method, path, _ in calls) + + +def test_history_restores_selection_retries_saved_input_and_cancels(browser_page): + page, url = browser_page + jobs = {A: job(A, "interrupted"), B: job(B, "running", title="Working score")} + calls = mock_api(page, jobs=jobs) + page.goto(url, wait_until="networkidle") + page.locator(f'[data-open="{A}"]').click() + page.wait_for_function("document.querySelector('#jobLabel').textContent.includes('aaaaaaaaaaaa')") + assert page.locator("#planDensity").input_value() == "0.75" + assert page.evaluate("localStorage.getItem('beatforge.lastJob')") == A + page.reload(wait_until="networkidle") + page.wait_for_function("document.querySelector('#jobLabel').textContent.includes('aaaaaaaaaaaa')") + page.locator(f'[data-retry="{A}"]').click() + page.wait_for_function("document.querySelector('#jobLabel').textContent.includes('cccccccccccc')") + assert jobs[C]["retryOf"] == A + page.locator(f'[data-cancel="{B}"]').click() + page.wait_for_function("!document.querySelector('[data-cancel=bbbbbbbbbbbb]')") + assert jobs[B]["status"] == "cancelled" + posts = [path for method, path, _ in calls if method == "POST"] + assert f"/api/jobs/{A}/retry" in posts + assert f"/api/jobs/{B}/cancel" in posts + assert "/api/generate" not in posts + + +def test_ab_switching_preserves_audio_time_and_saves_explicit_preference(browser_page): + page, url = browser_page + jobs = {A: job(A, summary=DEMO["summary"]), B: job(B, summary=DEMO["summary"])} + previews = {A: preview(A, "Hard"), B: preview(B, "Easy")} + calls = mock_api(page, jobs=jobs, previews=previews) + page.goto(url, wait_until="networkidle") + page.locator(f'[data-open="{A}"]').click() + page.wait_for_function("window.BeatForgePreview.getState()?.jobId === 'aaaaaaaaaaaa'") + page.locator("#compareControls summary").click() + page.locator("#compareJob").select_option(B) + page.locator("#compareLoad").click() + page.wait_for_function("!document.querySelector('#compareB').disabled") + page.locator("#previewSeek").fill("9.5") + page.locator("#previewSeek").dispatch_event("input") + page.locator("#compareB").click() + page.wait_for_function("window.BeatForgePreview.getState()?.jobId === 'bbbbbbbbbbbb' && window.BeatForgePreview.getState().notes === 20 && Math.abs(window.BeatForgePreview.getState().time - 9.5) < 0.1") + assert page.evaluate("window.BeatForgePreview.getState().notes") == len(DEMO["charts"]["Easy"]["colorNotes"]) + page.locator("#preferB").click() + page.wait_for_function("document.querySelector('#feedbackMessage').textContent.includes('Preference for B saved')") + body = next(body for method, path, body in reversed(calls) if method == "POST" and path == "/api/comparisons") + assert body["preferredJob"] == B + assert body["alternateJob"] == A + assert not any(path.endswith("/playtests") for _, path, _ in calls) + + +def test_feedback_error_is_visible_and_suggestions_require_explicit_apply(browser_page): + page, url = browser_page + suggestions = {"ready": True, "evidenceCount": 3, "reasons": ["Three charts were too dense."], "changedFields": {"density": {"from": 1, "to": 0.85}}, "suggestedPlan": {"density": 0.85, "style": "flow"}} + jobs = {A: job(A, summary=DEMO["summary"])} + calls = mock_api(page, jobs=jobs, previews={A: preview()}, suggestions=suggestions, feedback_error="Feedback could not be saved; your previous notes are preserved.") + page.goto(url, wait_until="networkidle") + assert page.locator("#planDensity").input_value() == "auto" + page.wait_for_function("!document.querySelector('#applySuggestion').disabled") + page.locator("#applySuggestion").click() + assert page.locator("#planDensity").input_value() == "0.85" + assert not any(path == "/api/generate" for _, path, _ in calls) + page.locator(f'[data-open="{A}"]').click() + page.wait_for_function("window.BeatForgePreview.getState()?.jobId === 'aaaaaaaaaaaa'") + page.locator("#rating-flow").select_option("3") + page.locator("#feedbackNotes").fill("The chorus felt cramped.") + page.locator('#feedbackForm button[type="submit"]').click() + page.wait_for_function("document.querySelector('#feedbackMessage').textContent.includes('could not be saved')") + assert not any(path.endswith("/playtests") for _, path, _ in calls) + assert page.evaluate("window.BeatForgeApp.getStudioState().job.releaseGate.evidenceCount") == 0 diff --git a/tests/test_webmcp.py b/tests/test_webmcp.py index f864cc4..18973a3 100644 --- a/tests/test_webmcp.py +++ b/tests/test_webmcp.py @@ -1,6 +1,8 @@ """WebMCP challenge contracts for the browser collaboration surface.""" from pathlib import Path +import json +import re from fastapi.testclient import TestClient @@ -23,8 +25,11 @@ def test_webmcp_uses_standard_document_registration_and_named_tools() -> None: "generate_beatmap", "review_current_beatmap", "record_human_playtest", + "get_chart_preview", + "get_learning_summary", + "record_mapping_feedback", ): - assert f'name: "{name}"' in WEBMCP + assert re.search(r'name\s*:\s*"' + re.escape(name) + r'"', WEBMCP) assert "inputSchema" in WEBMCP assert "window.BeatForgeWebMcp" in WEBMCP @@ -49,10 +54,14 @@ def test_webmcp_script_is_served_by_the_studio() -> None: assert "document.modelContext.registerTool" in response.text -def test_demo_is_explicitly_rights_safe_and_not_a_real_download() -> None: +def test_demo_download_contains_real_rights_safe_chart_assets() -> None: assert "Synthetic 30-second groove" in WEB - assert "Browser preview · no ZIP" in WEB + assert "Download sample ZIP" in WEB assert "Human headset evidence is still required" in WEB + demo = json.loads((ROOT / "web" / "assets" / "demo" / "preview.json").read_text()) + assert demo["source"] == "synthetic-authored-score" + assert len(demo["charts"]) == 5 + assert (ROOT / "web" / "assets" / "demo" / "map.zip").is_file() def test_metadata_lookup_is_explicitly_preview_only() -> None: diff --git a/tools/build_demo_assets.py b/tools/build_demo_assets.py new file mode 100644 index 0000000..f773917 --- /dev/null +++ b/tools/build_demo_assets.py @@ -0,0 +1,204 @@ +"""Build an original 30-second score and real, validated browser demo charts. + +This fixture invokes canonical choreography directly with a mathematically +authored sample timeline. It does not pretend to analyze a recording, run the +official-corpus pipeline, use a trained model, or provide headset evidence. +""" + +from __future__ import annotations + +import argparse +import copy +import hashlib +import json +import shutil +import sys +import tempfile +import wave +from pathlib import Path +from typing import Any + +import numpy as np + +ROOT = Path(__file__).resolve().parents[1] +SCRIPTS = ROOT / "skills" / "beat-saber-mapping" / "scripts" +for directory in (ROOT / "src", SCRIPTS): + if str(directory) not in sys.path: + sys.path.insert(0, str(directory)) + +from choreography import generate_all +from beatforge_core import encode_ogg +from generate_map import DAFT_PUNK_COLOR_SCHEME, info_dat, write_default_cover +from mapping_plan import build_section_plan, normalize_mapping_plan +from timing_export import project_map_timing, project_plan_timing +from validate_map import validate_package +from beatforge.premium import package_map, summarize_map +from beatforge.preview import preview_payload + +SAMPLE_RATE = 44100 +BPM = 120.0 +DURATION = 30.0 +SEED = 20260911 +DIFFICULTIES = ("Easy", "Normal", "Hard", "Expert", "ExpertPlus") + + +def write_json(path: Path, value: Any) -> None: + path.write_text(json.dumps(value, indent=2) + "\n", encoding="utf-8") + + +def synthesize_score() -> tuple[np.ndarray, dict[str, Any], dict[str, Any]]: + """Synthesize every instrument on the same integer sample clock as the score.""" + length = int(SAMPLE_RATE * DURATION) + stems = {name: np.zeros(length, dtype=np.float64) for name in ("drums", "bass", "piano")} + rng = np.random.default_rng(SEED) + timeline: dict[float, dict[str, Any]] = {} + + def add(beat: float, instrument: str, samples: np.ndarray, strength: float, sustain: float = 0) -> None: + start = round(beat * 60 / BPM * SAMPLE_RATE) + count = min(len(samples), length - start) + if count <= 0: + return + stems[instrument][start:start + count] += samples[:count] + item = timeline.setdefault(beat, {"beat": beat, "snappedBeat": beat, "sample": start, "timeSeconds": start / SAMPLE_RATE, "strength": 0.0, "sustainBeats": 0.0, "instruments": []}) + item["instruments"].append(instrument) + if strength > item["strength"]: + item.update(strength=strength, layer=instrument) + item["sustainBeats"] = max(item["sustainBeats"], sustain) + + for half_beat in range(120): + beat = half_beat / 2 + chorus = 24 <= beat < 40 or 48 <= beat < 58 + if half_beat % 2 == 0: + t = np.arange(round(0.22 * SAMPLE_RATE)) / SAMPLE_RATE + # A pitched kick settles from its transient onto the bass root. + phase = 2 * np.pi * (48 * t + 6.5 * (1 - np.exp(-t * 28))) + kick = np.sin(phase) * np.exp(-t * 21) * 0.65 + add(beat, "drums", kick, 1.8 if beat % 4 == 0 else 1.2) + if int(beat) % 2 == 1: + t = np.arange(round(0.13 * SAMPLE_RATE)) / SAMPLE_RATE + snare = (rng.normal(0, 1, len(t)) * 0.16 + np.sin(2 * np.pi * 185 * t) * 0.12) * np.exp(-t * 28) + add(beat, "drums", snare, 1.4) + if beat >= 8: + t = np.arange(round(0.035 * SAMPLE_RATE)) / SAMPLE_RATE + noise = rng.normal(0, 1, len(t)) + hat = (noise - np.concatenate(([0], noise[:-1]))) * np.exp(-t * 125) * (0.035 if chorus else 0.024) + add(beat, "drums", hat, 0.72 if chorus else 0.5) + if half_beat % 2 == 0 and beat >= 4: + root = (55.0, 65.4064, 48.9994, 58.2705)[int(beat // 8) % 4] + t = np.arange(round(0.31 * SAMPLE_RATE)) / SAMPLE_RATE + bass = (np.sin(2 * np.pi * root * t) + 0.22 * np.sin(2 * np.pi * root * 2 * t)) * np.minimum(t / 0.004, 1) * np.exp(-t * 11) * 0.25 + add(beat, "bass", bass, 1.0, 0.5) + if beat >= 16 and half_beat % (1 if chorus else 4) == 0 and beat < 58: + degree = (0, 7, 12, 3, 10, 7, 3, 12)[half_beat % 8] + frequency = 220 * 2 ** (degree / 12) + t = np.arange(round(0.2 * SAMPLE_RATE)) / SAMPLE_RATE + note = (np.sin(2 * np.pi * frequency * t) + 0.2 * np.sin(2 * np.pi * frequency * 2 * t)) * np.minimum(t / 0.003, 1) * np.exp(-t * 18) * 0.095 + add(beat, "piano", note, 0.88 if chorus else 0.7, 0.25) + + events = [] + for beat, item in sorted(timeline.items()): + start = item["sample"] + energies = {name: float(np.sqrt(np.mean(stem[start:min(start + 2048, length)] ** 2))) for name, stem in stems.items()} + scale = max(energies.values(), default=1.0) or 1.0 + item["stemEnergy"] = {name: round(value / scale, 6) for name, value in energies.items()} + events.append(item) + mix = sum(stems.values()) + mix /= max(1.0, float(np.max(np.abs(mix))) / 0.85) + fade = round(SAMPLE_RATE * 0.3) + mix[-fade:] *= np.linspace(1, 0, fade) + stereo = np.column_stack((mix, mix)).astype(np.float32) + checkpoints = [{"label": label, "beat": beat, "sample": round(beat * 60 / BPM * SAMPLE_RATE), "timeSeconds": beat * 60 / BPM, "source": "synthetic-authored-score"} for label, beat in (("start", 4), ("middle", 30), ("end", 56))] + analysis = { + "schemaVersion": 1, "status": "timing_verified", "source": "synthetic-authored-score", + "sampleRate": SAMPLE_RATE, "durationSeconds": DURATION, "durationSamples": length, "bpm": BPM, + "gridSource": "synthetic-authored-score", + "beatGrid": [ + # The score is authored on half beats, exactly 11025 samples apart. + # Finer decisions interpolate these anchors without accumulated rounding drift. + {"beat": tick / 2, "sample": round(tick / 2 * 60 / BPM * SAMPLE_RATE), "timeSeconds": round(tick / 2 * 60 / BPM * SAMPLE_RATE) / SAMPLE_RATE} + for tick in range(round(DURATION * BPM / 60 * 2)) + ], + "tempoRegions": [{"startBeat": 0, "endBeat": DURATION * BPM / 60, "startSample": 0, "endSample": length, "bpm": BPM}], + "offsetSeconds": 0.0, "events": events, + "timingEvidence": {"method": "Every oscillator and drum is placed at round(beat * 60 / bpm * sampleRate)", "authoredSampleClock": True, "trackerAnalysisPerformed": False, "humanListeningVerified": False}, + "clickTrackEvidence": {"source": "authored-score-checkpoints", "checkpoints": checkpoints}, + } + sections = {"source": "synthetic-authored-score", "sections": [ + {"id": "section-000", "label": "Intro", "startBeat": 0, "endBeat": 8, "intensity": 0.3}, + {"id": "section-001", "label": "Verse", "startBeat": 8, "endBeat": 24, "intensity": 0.5}, + {"id": "section-002", "label": "Chorus", "startBeat": 24, "endBeat": 40, "intensity": 0.82}, + {"id": "section-003", "label": "Verse", "startBeat": 40, "endBeat": 48, "intensity": 0.5}, + {"id": "section-004", "label": "Chorus", "startBeat": 48, "endBeat": 60, "intensity": 0.82}, + ]} + return stereo, analysis, sections + + +def build(output: Path = ROOT / "web" / "assets" / "demo") -> dict[str, Any]: + output = output.resolve() + output.mkdir(parents=True, exist_ok=True) + # All temporary map files live beneath the selected output, never in a game/cache folder. + with tempfile.TemporaryDirectory(prefix="demo-build-", dir=output) as temporary: + folder = Path(temporary) + reports = folder / "_beatforge" + reports.mkdir() + audio, analysis, sections = synthesize_score() + wav_path = folder / "source.wav" + with wave.open(str(wav_path), "wb") as wav: + wav.setnchannels(2) + wav.setsampwidth(2) + wav.setframerate(SAMPLE_RATE) + wav.writeframes((np.clip(audio, -1, 1) * 32767).astype(" None: OUTPUT.mkdir(parents=True, exist_ok=True) html = (SOURCE / "index.html").read_text(encoding="utf-8") - html = html.replace('src="/web/webmcp.js"', 'src="webmcp.js"') + html = html.replace('', '\n ') + html = html.replace('src="/web/', 'src="') + html = html.replace('href="/web/', 'href="') html = html.replace('"/assets/', '"assets/') html = html.replace("'/assets/", "'assets/") (OUTPUT / "index.html").write_text(html, encoding="utf-8") - shutil.copy2(SOURCE / "webmcp.js", OUTPUT / "webmcp.js") + for file in SOURCE.iterdir(): + if file.suffix in {'.js', '.css'}: + shutil.copy2(file, OUTPUT / file.name) assets = OUTPUT / "assets" - if assets.exists(): - shutil.rmtree(assets) - shutil.copytree(SOURCE / "assets", assets) + shutil.copytree(SOURCE / "assets", assets, dirs_exist_ok=True) if __name__ == "__main__": diff --git a/tools/doctor.py b/tools/doctor.py new file mode 100644 index 0000000..cf47610 --- /dev/null +++ b/tools/doctor.py @@ -0,0 +1,59 @@ +"""Read-only dependency checks for the local Studio. Does not download models.""" +from __future__ import annotations + +import argparse +import importlib +import importlib.util +import json +import subprocess +import sys +from pathlib import Path + + +def inspect() -> dict: + checks = [] + for module in ("beatforge", "fastapi", "uvicorn", "numpy", "soundfile", "ortools.sat.python.cp_model"): + try: + importlib.import_module(module) + checks.append({"name": module, "ok": True, "required": True}) + except Exception as error: + checks.append({"name": module, "ok": False, "required": True, "detail": str(error), "action": 'Run Start-BeatForge.ps1 -Setup or python -m pip install -e ".[dev,studio]".'}) + try: + import imageio_ffmpeg + result = subprocess.run([imageio_ffmpeg.get_ffmpeg_exe(), "-version"], capture_output=True, text=True, timeout=15) + if result.returncode: + raise RuntimeError(result.stderr[:300]) + checks.append({"name": "FFmpeg", "ok": True, "required": True, "detail": result.stdout.splitlines()[0]}) + except Exception as error: + checks.append({"name": "FFmpeg", "ok": False, "required": True, "detail": str(error), "action": "Install imageio-ffmpeg into this environment."}) + for module in ("torch", "beat_this", "demucs"): + available = importlib.util.find_spec(module) is not None + checks.append({"name": module, "ok": available, "required": False, "detail": "Package found; model weights and runtime have not been tested." if available else "Optional package missing. RL requires Torch; uncertain analysis requests timing anchors."}) + try: + from beatforge.premium import corpus_database + available = corpus_database().is_file() + checks.append({"name": "Official corpus", "ok": available, "required": False, "detail": "Database found; completeness is checked during generation." if available else "Full premium generation needs the local official corpus. The bundled sample works without it."}) + except Exception as error: + checks.append({"name": "Official corpus", "ok": False, "required": False, "detail": str(error)}) + return {"python": sys.executable, "studioReady": all(row["ok"] for row in checks if row["required"]), "checks": checks} + + +def main() -> int: + parser = argparse.ArgumentParser(description=__doc__) + parser.add_argument("--json", action="store_true") + args = parser.parse_args() + report = inspect() + if args.json: + print(json.dumps(report, indent=2)) + else: + print(f"Python: {report['python']}") + for item in report["checks"]: + label = "OK" if item["ok"] else "MISSING" if item["required"] else "OPTIONAL" + print(f"[{label}] {item['name']}: {item.get('detail', '')}") + if item.get("action"): + print(f" {item['action']}") + return 0 if report["studioReady"] else 1 + + +if __name__ == "__main__": + raise SystemExit(main()) diff --git a/web/assets/demo/map.zip b/web/assets/demo/map.zip new file mode 100644 index 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zL6~9a;dXChnQ@;n{KDVweDjh6sI;SlVyasKm=6c03gv7x6hKKo#e=EQ8SIB#FyZsR z*`%f*%s9yd|jya6@8wHp4I}RB3OT|H`0&;2CV> zX>9J3u_9kz|8$H?$sxf?oyK%9H0HcCpi;Mqs`&YqnEkz82B#|H0aR( E1Hhu)5C8xG literal 0 HcmV?d00001 diff --git a/web/index.html b/web/index.html index 763ba3c..4a54032 100644 --- a/web/index.html +++ b/web/index.html @@ -210,10 +210,14 @@ button:focus-visible, input:not([type="file"]):focus-visible, select:focus-visible, a:focus-visible { outline-width: 3px; } } +

+ Preview + My runs + Improve Checking Beat Saber library
@@ -273,7 +277,7 @@

Start a mapping run

-

Generate always packs a download. Unverified timing does not block it. The AI timing buttons are an optional check, not a gate. Release candidate is not required to download. BeatForge will not fill either gap silently.

+

Generate analyzes timing and checks the chart. If timing needs confirmation, use anchors or explicitly continue unverified. Structural errors block installation. BeatForge will not fill either gap silently.

@@ -285,9 +289,23 @@

Start a mapping run

Give the agent musical intent in plain language. It can apply a typed plan, start the visible run, read safety gates, and record evidence you explicitly supply after playing. The agent never receives your song audio through these tools and never invents a clearance.

+
+ + + + + + + + +
+
Try “lighter verses, denser chorus, follow drums, no bombs”. Explicit controls override the brief. Other prose is saved as a note.
+
+
Generation engine

Premium uses musical controls and deterministic constraint solving.

+
- The demo uses a synthetic 30-second groove and does not create a Beat Saber pack. + Explore a real synthetic 30-second groove and download its sample map.
Exposed tools
Waiting for WebMCP…
@@ -295,6 +313,20 @@

Start a mapping run

+
+
Listen, inspect, revise

Map preview

Load a sample or completed run
+

Hear the song. See the movement.

+ The chart preview requires a browser with canvas support. + +
0:00 / 0:00
+ +
+
Revise a section

Complete a local mapping run to revise its sections.

+
+
+

Preview geometry helps inspect a chart. Beat Saber scoring, spawn behavior and headset comfort still need a real playtest.

+
+

My runs

Your local runs will appear here.
@@ -304,7 +336,7 @@

Start a mapping run

No events yet.

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